Method and apparatus for streaming data

ABSTRACT

A terminal for receiving streaming data may receive information of a plurality of different quality versions of an image content; request, based on the information, a server for a version of the image content from among the plurality of different quality versions of the image content; when the requested version of the image content and artificial intelligence (AI) data corresponding to the requested version of the image content are received, determines whether to perform AI upscaling on the received version of the image content, based on the AI data; and based on a result of the determining whether to perform AI upscaling, performs AI upscaling on the received version of the image content through a upscaling deep neural network (DNN) that is trained jointly with a downscaling DNN of the server.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 16/659,061, filed Oct. 21, 2019, which is based onand claims priority under 35 U.S.C. § 119 to Korean Patent ApplicationNos. 10-2018-0125406, filed on Oct. 19, 2018, and 10-2019-0041110, filedon Apr. 8, 2019 and 10-2019-0077250, filed on Jun. 27, 2019, in theKorean Intellectual Property Office, the disclosures of which areincorporated herein by reference in their entireties.

BACKGROUND 1. Field

The disclosure relates to data streaming technology. More particularly,the disclosure relates to a method and apparatus for adaptivelystreaming image data artificial intelligence (AI)-encoded by using adeep neural network (DNN).

2. Description of Related Art

A scheme for transmitting image data through a network includes adownload scheme and a streaming scheme. The streaming scheme refers to ascheme for transmitting, by a server, image data in real time, andreproducing, by a terminal, received image data in real time.

Unlike the download scheme in which reproduction of image data isstarted after the image data is completely transceived, i.e., completelytransmitted and received, according to the streaming scheme, image datais transceived and reproduced in real time via a logic channelestablished between a server and a terminal, and thus a Quality ofService (QoS) of image data reproduction may be maintained whilereflecting a change in a streaming environment.

Artificial intelligence (AI) systems are computer systems forimplementing human-level intelligence. Unlike general rule-based smartsystems, the AI systems autonomously learn and make decisions, and thusimprove their capabilities. The more the AI systems are used, the morerecognition rates of the AI systems increase and the more accurately theAI systems understand user preferences. As such, the general rule-basedsmart systems may be replaced by deep-learning-based AI systems.

As interest in the AI systems increases, many attempts are activelybeing made to apply the AI systems to various technology fields. Forexample, research is being conducted to converge the AI systems withtechnology fields including image processing, data processing, and thelike.

SUMMARY

Provided are a method and apparatus for streaming data that isartificial intelligence (AI)-encoded by using a deep neural network(DNN).

Additional aspects will be set forth in part in the description whichfollows and, in part, will be apparent from the description, or may belearned by practice of the presented embodiments of the disclosure.

According to embodiment of the disclosure, there is provided a method ofstreaming data, including: receiving information of a plurality ofdifferent quality versions of an image content; requesting, based on theinformation, a server to transmit a first version of the image contentto a terminal, from among the plurality of different quality versions ofthe image content; receiving the first version of the image content andartificial intelligence (AI) data corresponding to the first version ofthe image content; determining whether to perform AI upscaling on thefirst version of the image content, based on the AI data; based on aresult of the determining whether to perform the AI upscaling,performing AI upscaling on the first version of the image contentthrough an upscaling deep neural network (DNN) that is trained jointlywith a downscaling DNN of the server; confirming a state of a networkbetween the terminal and the server; and requesting the server totransmit a second version of the image content to the terminal, fromamong the plurality of different versions of the image content,according to the information of the plurality of different qualityversions of the image content and the state of the network.

The method may further include determining, based on the AI data,whether AI downscaling has been performed on the first version of theimage content through the downscaling DNN of the server, and wherein,when it is confirmed that the AI downscaling has been performed on thefirst version of the image content, the determining of whether the AIdownscaling has been performed may include determining to perform the AIupscaling on the first version of the image content.

The information of the plurality of different quality versions of theimage content may include quality information and AI scale conversioninformation of the plurality of different quality versions of the imagecontent, and the requesting the server to transmit the second version ofthe image content may include requesting the second version of the imagecontent corresponding to the state of the network, based on either oneor both of the quality information, and the AI scale conversioninformation.

The method may further include determining the second version of theimage content corresponding to the state of the network, based oncapability information including information indicating whether the AIupscaling is supported by the terminal and information about an AIupscale level supported by the terminal.

The server may be a content provider server, wherein the method mayfurther include requesting a service server for the information of theplurality of different quality versions of the image content, andwherein the receiving the information of the plurality of differentquality versions of the image content may include receiving, from theservice server, the information of the plurality of different qualityversions of the image content and an identifier of the content providerserver.

According to embodiment of the disclosure, there is provided a method ofstreaming data, including: receiving, from a terminal, a request for afirst version of an image content from among a plurality of differentquality versions of the image content of a server; in response to therequest, transmitting, to the terminal, artificial intelligence (AI)data and the first version of the image content that has been AI encodedthrough a downscaling DNN of the server that is trained jointly with aupscaling DNN of the terminal; and receiving, from the terminal, arequest for a second version of the image content from among theplurality of different versions of the image content, according to astate of a network between the terminal and the server.

The AI data may include information about the downscaling DNN that hasbeen applied to the AI-encoded image data.

The receiving the request for the second version of the image contentmay include receiving the request for the second version of the imagecontent that may correspond to the state of the network and may bedetermined based on either one or both of AI scale conversioninformation and quality information of each of the plurality ofdifferent versions of the image content.

The second version of the image content corresponds to the state of thenetwork and is determined from among the plurality of different versionsof the image content, based on capability information comprisinginformation indicating whether AI upscaling is supported by the terminaland information about an AI upscale level supported by the terminal.

The method may further include providing the terminal with an identifierof the server.

According to embodiment of the disclosure, a terminal for receivingstreaming data, including: a memory storing one or more instructions;and at least one processor configured to execute the one or moreinstructions to: receive information of a plurality of different qualityversions of an image content; request, based on the information, aserver to transmit a first version of the image content, from among theplurality of different versions of the image content; receive the firstversion of the image content and artificial intelligence (AI) datacorresponding to the first version of the image content; determinewhether to perform AI upscaling on the first version of the imagecontent, based on the AI data; based on a result of the determiningwhether to perform the AI upscaling, perform the AI upscaling on thefirst version of the image content through an upscaling deep neuralnetwork (DNN) of the terminal that is trained jointly with a downscalingDNN of the server; confirm a state of a network between the terminal andthe server; and request, based on the information, the server totransmit a second version of the image content to the terminal, fromamong the plurality of different versions of the image content,according to the information of the plurality of different qualityversions of the image content and the state of the network.

The at least one processor may be further configured to: determine,based on the AI data, whether AI downscaling has been performed on thefirst version of the image content through the downscaling DNN of theserver; and when it is confirmed that the AI downscaling has beenperformed on the first version of the image content, determine toperform the AI upscaling on the first version of the image content.

The information of the plurality of different quality versions of theimage content may include quality information and AI scale conversioninformation of the plurality of different quality versions of the imagecontent, and wherein the at least one processor may be furtherconfigured to execute the one or more instructions to request the secondversion of the image content corresponding to the state of the network,based on either one or both of the quality information and the AI scaleconversion information.

The at least one processor may be further configured to: determine thesecond version of the image content corresponding to the state of thenetwork, based on capability information comprising informationindicating whether AI upscaling is supported by the terminal andinformation about an AI upscale level supported by the terminal.

The server may be a content provider server, and the at least oneprocessor may be further configured to execute the one or moreinstructions to request a service server to provide the terminal withthe information of the plurality of different quality versions of theimage content, and to receive, from the service server an identifier ofthe content provider server and the information of the plurality ofdifferent quality versions of the image content.

According to embodiment of the disclosure, there is provided server forstreaming data, including: a memory storing one or more instructions;and at least one processor configured to execute the one or moreinstructions to: receive, from a terminal, a request for a first versionof an image content, from among a plurality of different qualityversions of the image content of a server; in response to the request,transmit, to the terminal, artificial intelligence (AI) data and thefirst version of the image content that has been AI encoded through adownscaling deep neural network (DNN) that is trained jointly with anupscaling DNN of the terminal; and receive, from the terminal, a requestfor a second version of the image content from the plurality ofdifferent versions of the image content, according to a state of anetwork between the terminal and the server.

The AI data may include information about the downscaling DNN that hasbeen applied to the AI-encoded image data.

The at least one processor may be further configured to execute the oneor more instructions to receive the request for the second version ofthe image content that corresponds to the state of the network and isdetermined based on either one or both of AI scale conversioninformation and quality information of each of the plurality ofdifferent versions of the image content.

The second version of the image content may correspond to the state ofthe network and may be determined based on capability informationcomprising information indicating whether AI upscaling is supported bythe terminal and information about an AI upscale level supported by theterminal.

The at least one processor may be further configured to execute the oneor more instructions to provide the terminal with an identifier of theserver.

According to embodiment of the disclosure, there is provided anon-transitory computer-readable recording medium having recordedthereon a program for executing the method of steaming data.

According to embodiment of the disclosure, there is provided a terminalfor streaming data, including: a memory storing one or moreinstructions; and at least one processor configured to execute the oneor more instructions to: receive, from a server, information of aplurality of different quality versions of an image content; determine astate of a network between the terminal and the server; determine aversion of the image content, from among the plurality of differentquality versions of the image content, based on the information of theplurality of different quality versions of the image content and thestate of the network, and request the server to transmit the version ofthe image content to the terminal; receive, from the server, the versionof the image content and artificial intelligence (AI) data indicatingwhether the version of the image content is downscaled through adownscaling deep neural network (DNN) of the server; and process theversion of the image content based on the AI data.

According to embodiment of the disclosure, there is provided a serverfor streaming data, including: a memory storing one or moreinstructions; and at least one processor configured to execute the oneor more instructions to: provide a terminal with information of aplurality of different quality versions of a image content; receive,from the terminal, a request for a version of the image content, fromamong the plurality of different quality versions of the image content,according to a state of a network between the terminal and the server;and provide the terminal with the requested version of the image contentand artificial intelligence (AI) data indicating whether the requestedversion of the image content is downscaled through a downscaling deepneural network (DNN) of the server.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features, and advantages of certainembodiments of the disclosure will be more apparent from the followingdescription taken in conjunction with the accompanying drawings, inwhich:

A brief description of each drawing is provided to more fully understandthe drawing recited in the present specification.

FIG. 1 is a diagram for describing an artificial intelligence (AI)encoding process and an AI decoding process, according to embodiments;

FIG. 2 is a block diagram of a configuration of an AI decoding apparatusaccording to embodiments;

FIG. 3 is a diagram showing a second deep neural network (DNN) forperforming AI up-scaling on a second image;

FIG. 4 is a diagram for describing a convolution operation by aconvolution layer;

FIG. 5 is a table showing a mapping relationship between several piecesof image-related information and several pieces of DNN settinginformation;

FIG. 6 is a diagram showing a second image including a plurality offrames;

FIG. 7 is a block diagram of a configuration of an AI encoding apparatusaccording to embodiments;

FIG. 8 is a diagram showing a first DNN for performing AI down-scalingon an original image;

FIG. 9 is a diagram for describing a method of training a first DNN anda second DNN;

FIG. 10 is a diagram for describing a training process of a first DNNand a second DNN by a training apparatus;

FIG. 11 is a diagram of an apparatus for performing AI down-scaling onan original image and an apparatus for performing AI up-scaling on asecond image;

FIG. 12 is a diagram for describing a concept of a streaming system,according to embodiments of the disclosure;

FIG. 13A is a flowchart for describing a method of streaming data, themethod being performed by a server, according to embodiments of thedisclosure;

FIG. 13B is a flowchart for describing a method of streaming data, themethod being performed by a terminal, according to embodiments of thedisclosure;

FIG. 14A is a flowchart for describing a method of streaming data, themethod being performed by a server, according to embodiments of thedisclosure;

FIG. 14B is a flowchart for describing a method of streaming data, themethod being performed by the terminal, according to embodiments of thedisclosure;

FIG. 15A is a flowchart for describing a method of streaming data, themethod being performed by a server, according to embodiments of thedisclosure;

FIG. 15B is a flowchart for describing a method of streaming data, themethod being performed by the terminal, according to embodiments of thedisclosure;

FIG. 16 is a diagram for describing a method of performing streamingbetween a server and a first terminal according to whether the firstterminal supports AI upscaling, according to embodiments of thedisclosure;

FIG. 17 is a diagram for describing a method of performing streamingbetween a server and a first terminal according to whether the firstterminal supports AI upscaling, according to embodiments of thedisclosure;

FIG. 18 is a diagram for describing a method, performed by the server,of streaming image data according to a capability of the terminal,according to embodiments of the disclosure;

FIG. 19 is a diagram for describing a method, performed by the server,of streaming image data according to a state of a network and acapability of the terminal, according to embodiments of the disclosure;

FIG. 20 is a diagram for describing a method, performed by the terminal,of streaming image data corresponding to a state of a network, based onadditional information and a capability, according to embodiments of thedisclosure;

FIG. 21 is a diagram for describing additional information provided forstreaming, according to embodiments of the disclosure;

FIG. 22 is a diagram for describing detail configuration of additionalinformation, according to embodiments of the disclosure;

FIG. 23 is a diagram for describing detail configuration of additionalinformation, according to embodiments of the disclosure;

FIG. 24 is a diagram for describing detail configuration of additionalinformation, according to embodiments of the disclosure;

FIG. 25 is a diagram for describing AI data and image data that arestreamed from a server to a terminal, according to embodiments of thedisclosure;

FIG. 26 is a diagram for describing a streaming system, according toembodiments of the disclosure;

FIG. 27 is a block diagram illustrating a configuration of a server,according to embodiments of the disclosure; and

FIG. 28 is a block diagram illustrating a configuration of a terminal,according to embodiments of the disclosure.

DETAILED DESCRIPTION

As the disclosure allows for various changes and numerous examples,particular embodiments will be illustrated in the drawings and describedin detail in the written description. However, this is not intended tolimit the disclosure to particular modes of practice, and it will beunderstood that all changes, equivalents, and substitutes that do notdepart from the spirit and technical scope of the disclosure areencompassed in the disclosure.

In the description of embodiments, detailed explanations of related artare omitted when it is deemed that they may unnecessarily obscure theessence of the disclosure. Also, numbers (for example, a first, asecond, and the like) used in the description of the specification areidentifier codes for distinguishing one element from another.

Also, in the present specification, it will be understood that whenelements are “connected” or “coupled” to each other, the elements may bedirectly connected or coupled to each other, but may alternatively beconnected or coupled to each other with an intervening elementtherebetween, unless specified otherwise.

In the present specification, regarding an element represented as a“unit” or a “module”, two or more elements may be combined into oneelement or one element may be divided into two or more elementsaccording to subdivided functions. In addition, each element describedhereinafter may additionally perform some or all of functions performedby another element, in addition to main functions of itself, and some ofthe main functions of each element may be performed entirely by anothercomponent.

Also, in the present specification, an ‘image’ or a ‘picture’ may denotea still image, a moving image including a plurality of consecutive stillimages (or frames), or a video.

Also, in the present specification, a deep neural network (DNN) is arepresentative example of an artificial neural network model simulatingbrain nerves, and is not limited to an artificial neural network modelusing an algorithm.

Also, in the present specification, a ‘parameter’ is a value used in anoperation process of each layer forming a neural network, and forexample, may include a weight used when an input value is applied to acertain operation expression. Here, the parameter may be expressed in amatrix form. The parameter is a value set as a result of training, andmay be updated through separate training data.

Also, in the present specification, a ‘first DNN’ indicates a DNN usedfor artificial intelligence (AI) down-scaling an image, and a ‘secondDNN’ indicates a DNN used for AI up-scaling an image.

Also, in the present specification, ‘IDNN setting information’ includesinformation related to an element constituting a DNN. ‘IDNN settinginformation’ includes the parameter described above as informationrelated to the element constituting the DNN. The first DNN or the secondDNN may be set by using the DNN setting information.

Also, in the present specification, an ‘original image’ denotes an imageto be an object of AI encoding, and a ‘first image’ denotes an imageobtained as a result of performing AI down-scaling on the original imageduring an AI encoding process. Also, a ‘second image’ denotes an imageobtained via first decoding during an AI decoding process, and a ‘thirdimage’ denotes an image obtained by AI up-scaling the second imageduring the AI decoding process.

Also, in the present specification, ‘AI down-scale’ denotes a process ofdecreasing resolution of an image based on AI, and ‘first encoding’denotes an encoding process according to an image compression methodbased on frequency transformation. Also, ‘first decoding’ denotes adecoding process according to an image reconstruction method based onfrequency transformation, and ‘AI up-scale’ denotes a process ofincreasing resolution of an image based on AI.

Expressions such as “at least one of,” when preceding a list ofelements, modify the entire list of elements and do not modify theindividual elements of the list. For example, the expression “at leastone of a, b or c” indicates only a, only b, only c, both a and b, both aand c, both b and c, all of a, b, and c, or variations thereof.

FIG. 1 is a diagram for describing an AI encoding process and an AIdecoding process, according to embodiments.

As described above, when resolution of an image remarkably increases,the throughput of information for encoding and decoding the image isincreased, and accordingly, a method for improving efficiency ofencoding and decoding of an image is required.

As shown in FIG. 1, according to embodiments of the disclosure, a firstimage 115 is obtained by performing AI down-scaling 110 on an originalimage 105 having high resolution. Then, first encoding 120 and firstdecoding 130 are performed on the first image 115 having relatively lowresolution, and thus a bitrate may be largely reduced compared to whenthe first encoding 120 and the first decoding 130 are performed on theoriginal image 105.

In FIG. 1, the first image 115 is obtained by performing the AIdown-scaling 110 on the original image 105 and the first encoding 120 isperformed on the first image 115 during the AI encoding process,according to embodiments. During the AI decoding process, AI encodingdata including AI data and image data, which are obtained as a result ofAI encoding is received, a second image 135 is obtained via the firstdecoding 130, and a third image 145 is obtained by performing AIup-scaling 140 on the second image 135.

Referring to the AI encoding process in detail, when the original image105 is received, the AI down-scaling 110 is performed on the originalimage 105 to obtain the first image 115 of certain resolution or certainquality. Here, the AI down-scaling 110 is performed based on AI, and AIfor the AI down-scaling 110 is trained jointly with AI for the AIup-scaling 140 of the second image 135. This is because the AIdown-scaling 110 and the AI up-scaling 120 have two competing objectivesof scaling-down and scaling-up an image, and therefore, when the AI forthe AI down-scaling 110 and the AI for the AI up-scaling 140 areseparately trained, a difference between the original image 105 which isan object of AI encoding and the third image 145 reconstructed throughAI decoding is increased.

In embodiments of the disclosure, the AI data may be used to maintainsuch a joint relationship during the AI encoding process and the AIdecoding process. Accordingly, the AI data obtained through the AIencoding process may include information indicating an up-scalingtarget, and during the AI decoding process, the AI up-scaling 140 isperformed on the second image 135 according to the up-scaling targetverified based on the AI data.

The AI for the AI down-scaling 110 and the AI for the AI up-scaling 140may be embodied as a DNN. As will be described later with reference toFIG. 9, because a first DNN and a second DNN are jointly trained bysharing loss information under a target, an AI encoding apparatus mayprovide target information used during joint training of the first DNNand the second DNN to an AI decoding apparatus, and the AI decodingapparatus may perform the AI up-scaling 140 on the second image 135 totarget resolution based on the provided target information.

Regarding the first encoding 120 and the first decoding 130 of FIG. 1,information amount of the first image 115 obtained by performing AIdown-scaling 110 on the original image 105 may be reduced through thefirst encoding 120. The first encoding 120 may include a process ofgenerating prediction data by predicting the first image 115, a processof generating residual data corresponding to a difference between thefirst image 115 and the prediction data, a process of transforming theresidual data of a spatial domain component to a frequency domaincomponent, a process of quantizing the residual data transformed to thefrequency domain component, and a process of entropy-encoding thequantized residual data. Such first encoding 120 may be performed viaone of image compression methods using frequency transformation, such asMPEG-2, H.264 Advanced Video Coding (AVC), MPEG-4, High Efficiency VideoCoding (HEVC), VC-1, VP8, VP9, and AOMedia Video 1 (AV1).

The second image 135 corresponding to the first image 115 may bereconstructed by performing the first decoding 130 on the image data.The first decoding 130 may include a process of generating the quantizedresidual data by entropy-decoding the image data, a process ofinverse-quantizing the quantized residual data, a process oftransforming the residual data of the frequency domain component to thespatial domain component, a process of generating the prediction data,and a process of reconstructing the second image 135 by using theprediction data and the residual data. Such first decoding 130 may beperformed via an image reconstruction method corresponding to one ofimage compression methods using frequency transformation, such asMPEG-2, H.264 AVC, MPEG-4, HEVC, VC-1, VP8, VP9, and AV1, which is usedin the first encoding 120.

The AI encoding data obtained through the AI encoding process mayinclude the image data obtained as a result of performing the firstencoding 120 on the first image 115, and the AI data related to the AIdown-scaling 110 of the original image 105. The image data may be usedduring the first decoding 130 and the AI data may be used during the AIup-scaling 140.

The image data may be transmitted in a form of a bitstream. The imagedata may include data obtained based on pixel values in the first image115, for example, residual data that is a difference between the firstimage 115 and prediction data of the first image 115. Also, the imagedata includes information used during the first encoding 120 performedon the first image 115. For example, the image data may includeprediction mode information, motion information, and information relatedto quantization parameter used during the first encoding 120. The imagedata may be generated according to a rule, for example, according to asyntax, of an image compression method used during the first encoding120, among MPEG-2, H.264 AVC, MPEG-4, HEVC, VC-1, VP8, VP9, and AV1.

The AI data is used in the AI up-scaling 140 based on the second DNN. Asdescribed above, because the first DNN and the second DNN are jointlytrained, the AI data includes information enabling the AI up-scaling 140to be performed accurately on the second image 135 through the secondDNN. During the AI decoding process, the AI up-scaling 140 may beperformed on the second image 135 to have targeted resolution and/orquality, based on the AI data.

The AI data may be transmitted together with the image data in a form ofa bitstream. Alternatively, according to embodiments, the AI data may betransmitted separately from the image data, in a form of a frame or apacket. The AI data and the image data obtained as a result of the AIencoding may be transmitted through the same network or throughdifferent networks.

FIG. 2 is a block diagram of a configuration of an AI decoding apparatus100 according to embodiments.

Referring to FIG. 2, the AI decoding apparatus 200 according toembodiments may include a receiver 210 and an AI decoder 230. Thereceiver 210 may include a communication interface 212, a parser 214,and an output interface 216. The AI decoder 230 may include a firstdecoder 232 and an AI up-scaler 234.

The receiver 210 receives and parses AI encoding data obtained as aresult of AI encoding, and distinguishably outputs image data and AIdata to the AI decoder 230.

The communication interface 212 receives the AI encoding data obtainedas the result of AI encoding through a network. The AI encoding dataobtained as the result of performing AI encoding includes the image dataand the AI data. The image data and the AI data may be received througha same type of network or different types of networks.

The parser 214 receives the AI encoding data received through thecommunication interface 212 and parses the AI encoding data todistinguish the image data and the AI data. For example, the parser 214may distinguish the image data and the AI data by reading a header ofdata obtained from the communication interface 212. According toembodiments, the parser 214 distinguishably transmits the image data andthe AI data to the output interface 216 via the header of the datareceived through the communication interface 212, and the outputinterface 216 transmits the distinguished image data and AI datarespectively to the first decoder 232 and the AI up-scaler 234. At thistime, it may be verified that the image data included in the AI encodingdata is image data generated via a codec (for example, MPEG-2, H.264AVC, MPEG-4, HEVC, VC-1, VP8, VP9, or AV1). In this case, correspondinginformation may be transmitted to the first decoder 232 through theoutput interface 216 such that the image data is processed via theverified codec.

According to embodiments, the AI encoding data parsed by the parser 214may be obtained from a data storage medium including a magnetic mediumsuch as a hard disk, a floppy disk, or a magnetic tape, an opticalrecording medium such as CD-ROM or DVD, or a magneto-optical medium suchas a floptical disk.

The first decoder 232 reconstructs the second image 135 corresponding tothe first image 115, based on the image data. The second image 135obtained by the first decoder 232 is provided to the AI up-scaler 234.According to embodiments, first decoding related information, such asprediction mode information, motion information, quantization parameterinformation, or the like included in the image data may be furtherprovided to the AI up-scaler 234.

Upon receiving the AI data, the AI up-scaler 234 performs AI up-scalingon the second image 135, based on the AI data. According to embodiments,the AI up-scaling may be performed by further using the first decodingrelated information, such as the prediction mode information, thequantization parameter information, or the like included in the imagedata.

The receiver 210 and the AI decoder 230 according to embodiments aredescribed as individual devices, but may be implemented through oneprocessor. In this case, the receiver 210 and the AI decoder 230 may beimplemented through an dedicated processor or through a combination ofsoftware and general-purpose processor such as application processor(AP), central processing unit (CPU) or graphic processing unit (GPU).The dedicated processor may be implemented by including a memory forimplementing embodiments of the disclosure or by including a memoryprocessor for using an external memory.

Also, the receiver 210 and the AI decoder 230 may be configured by aplurality of processors. In this case, the receiver 210 and the AIdecoder 230 may be implemented through a combination of dedicatedprocessors or through a combination of software and general-purposeprocessors such as AP, CPU or GPU. Similarly, the AI up-scaler 234 andthe first decoder 232 may be implemented by different processors.

The AI data provided to the AI up-scaler 234 includes informationenabling the second image 135 to be processed via AI up-scaling. Here,an up-scaling target should correspond to down-scaling of a first DNN.Accordingly, the AI data includes information for verifying adown-scaling target of the first DNN.

Examples of the information included in the AI data include differenceinformation between resolution of the original image 105 and resolutionof the first image 115, and information related to the first image 115.

The difference information may be expressed as information about aresolution conversion degree of the first image 115 compared to theoriginal image 105 (for example, resolution conversion rateinformation). Also, because the resolution of the first image 115 isverified through the resolution of the reconstructed second image 135and the resolution conversion degree is verified accordingly, thedifference information may be expressed only as resolution informationof the original image 105. Here, the resolution information may beexpressed as vertical/horizontal sizes or as a ratio (16:9, 4:3, or thelike) and a size of one axis. Also, when there is pre-set resolutioninformation, the resolution information may be expressed in a form of anindex or flag.

The information related to the first image 115 may include informationabout either one or both of a bitrate of the image data obtained as theresult of performing first encoding on the first image 115, and a codectype used during the first encoding of the first image 115.

The AI up-scaler 234 may determine the up-scaling target of the secondimage 135, based on either one or both of the difference information,and the information related to the first image 115, which are includedin the AI data. The up-scaling target may indicate, for example, to whatdegree resolution is to be up-scaled for the second image 135. When theup-scaling target is determined, the AI up-scaler 234 performs AIup-scaling on the second image 135 through a second DNN to obtain thethird image 145 corresponding to the up-scaling target.

Before describing a method, performed by the AI up-scaler 234, ofperforming AI up-scaling on the second image 135 according to theup-scaling target, an AI up-scaling process through the second DNN willbe described with reference to FIGS. 3 and 4.

FIG. 3 is a diagram showing a second DNN 300 for performing AIup-scaling on the second image 135, and FIG. 4 is a diagram fordescribing a convolution operation in a first convolution layer 310 ofFIG. 3.

As shown in FIG. 3, the second image 135 is input to the firstconvolution layer 310. 3×3×4 indicated in the first convolution layer310 shown in FIG. 3 indicates that a convolution process is performed onone input image by using four filter kernels having a size of 3×3. Fourfeature maps are generated by the four filter kernels as a result of theconvolution process. Each feature map indicates inherent characteristicsof the second image 135. For example, each feature map may represent avertical direction characteristic, a horizontal directioncharacteristic, or an edge characteristic, etc. of the second image 135.

A convolution operation in the first convolution layer 310 will bedescribed in detail with reference to FIG. 4.

One feature map 450 may be generated through multiplication and additionbetween parameters of a filter kernel 430 having a size of 3×3 used inthe first convolution layer 310 and corresponding pixel values in thesecond image 135. Four filter kernels are used in the first convolutionlayer 310, and four feature maps may be generated through theconvolution operation using the four filter kernels.

I1 through I49 indicated in the second image 135 in FIG. 4 indicatepixels in the second image 135, and F1 through F9 indicated in thefilter kernel 430 indicate parameters of the filter kernel 430. Also, M1through M9 indicated in the feature map 450 indicate samples of thefeature map 450.

In FIG. 4, the second image 135 includes 49 pixels, but the number ofpixels is only an example and when the second image 135 has a resolutionof 4 K, the second image 135 may include, for example, 3840×2160 pixels.

During a convolution operation process, pixel values of I1, I2, I3, I8,I9, I10, I15, I16, and I17 of the second image 135 and F1 through F9 ofthe filter kernels 430 are respectively multiplied, and a value ofcombination (for example, addition) of result values of themultiplication may be assigned as a value of M1 of the feature map 450.When a stride of the convolution operation is 2, pixel values of I3, I4,I5, I10, I11, I12, I17, I18, and I19 of the second image 135 and F1through F9 of the filter kernels 430 are respectively multiplied, andthe value of the combination of the result values of the multiplicationmay be assigned as a value of M2 of the feature map 450.

While the filter kernel 430 moves along the stride to the last pixel ofthe second image 135, the convolution operation is performed between thepixel values in the second image 135 and the parameters of the filterkernel 430, and thus the feature map 450 having a certain size may begenerated.

According to the present disclosure, values of parameters of a secondDNN, for example, values of parameters of a filter kernel used inconvolution layers of the second DNN (for example, F1 through F9 of thefilter kernel 430), may be optimized through joint training of a firstDNN and the second DNN. As described above, the AI up-scaler 234 maydetermine an up-scaling target corresponding to a down-scaling target ofthe first DNN based on AI data, and determine parameters correspondingto the determined up-scaling target as the parameters of the filterkernel used in the convolution layers of the second DNN.

Convolution layers included in the first DNN and the second DNN mayperform processes according to the convolution operation processdescribed with reference to FIG. 4, but the convolution operationprocess described with reference to FIG. 4 is only an example and is notlimited thereto.

Referring back to FIG. 3, the feature maps output from the firstconvolution layer 310 may be input to a first activation layer 320.

The first activation layer 320 may assign a non-linear feature to eachfeature map. The first activation layer 320 may include a sigmoidfunction, a Tan h function, a rectified linear unit (ReLU) function, orthe like, but is not limited thereto.

The first activation layer 320 assigning the non-linear featureindicates that at least one sample value of the feature map, which is anoutput of the first convolution layer 310, is changed. Here, the changeis performed by applying the non-linear feature.

The first activation layer 320 determines whether to transmit samplevalues of the feature maps output from the first convolution layer 310to a second convolution layer 330. For example, some of the samplevalues of the feature maps are activated by the first activation layer320 and transmitted to the second convolution layer 330, and some of thesample values are deactivated by the first activation layer 320 and nottransmitted to the second convolution layer 330. The intrinsiccharacteristics of the second image 135 represented by the feature mapsare emphasized by the first activation layer 320.

Feature maps 325 output from the first activation layer 320 are input tothe second convolution layer 330. One of the feature maps 325 shown inFIG. 3 is a result of processing the feature map 450 described withreference to FIG. 4 in the first activation layer 320.

3×3×4 indicated in the second convolution layer 330 indicates that aconvolution process is performed on the feature maps 325 by using fourfilter kernels having a size of 3×3. An output of the second convolutionlayer 330 is input to a second activation layer 340. The secondactivation layer 340 may assign a non-linear feature to input data.

Feature maps 345 output from the second activation layer 340 are inputto a third convolution layer 350. 3×3×1 indicated in the thirdconvolution layer 350 shown in FIG. 3 indicates that a convolutionprocess is performed to generate one output image by using one filterkernel having a size of 3×3. The third convolution layer 350 is a layerfor outputting a final image and generates one output by using onefilter kernel. According to embodiments of the disclosure, the thirdconvolution layer 350 may output the third image 145 as a result of aconvolution operation.

There may be a plurality of pieces of DNN setting information indicatingthe numbers of filter kernels of the first, second, and thirdconvolution layers 310, 330, and 350 of the second DNN 300, a parameterof filter kernels of the first, second, and third convolution layers310, 330, and 350 of the second DNN 300, and the like, as will bedescribed later, and the plurality of pieces of DNN setting informationmay be connected to a plurality of pieces of DNN setting information ofa first DNN. The connection between the plurality of pieces of DNNsetting information of the second DNN and the plurality of pieces of DNNsetting information of the first DNN may be realized via joint trainingof the first DNN and the second DNN.

In FIG. 3, the second DNN 300 includes three convolution layers (thefirst, second, and third convolution layers 310, 330, and 350) and twoactivation layers (the first and second activation layers 320 and 340),but this is only an example, and the numbers of convolution layers andactivation layers may vary according to embodiments. Also, according toembodiments, the second DNN 300 may be implemented as a recurrent neuralnetwork (RNN). In this case, a convolutional neural network (CNN)structure of the second DNN 300 according to embodiments of thedisclosure is changed to an RNN structure.

According to embodiments, the AI up-scaler 234 may include at least onearithmetic logic unit (ALU) for the convolution operation and theoperation of the activation layer described above. The ALU may beimplemented as a processor. For the convolution operation, the ALU mayinclude a multiplier that performs multiplication between sample valuesof the second image 135 or the feature map output from previous layerand sample values of the filter kernel, and an adder that adds resultvalues of the multiplication. Also, for the operation of the activationlayer, the ALU may include a multiplier that multiplies an input samplevalue by a weight used in a pre-determined sigmoid function, a Tan hfunction, or an ReLU function, and a comparator that compares amultiplication result and a certain value to determine whether totransmit the input sample value to a next layer.

Hereinafter, a method, performed by the AI up-scaler 234, of performingthe AI up-scaling on the second image 135 according to the up-scalingtarget will be described.

According to embodiments, the AI up-scaler 234 may store a plurality ofpieces of DNN setting information settable in a second DNN.

Here, the DNN setting information may include information about any oneor any combination of the number of convolution layers included in thesecond DNN, the number of filter kernels for each convolution layer, anda parameter of each filter kernel. The plurality of pieces of DNNsetting information may respectively correspond to various up-scalingtargets, and the second DNN may operate based on DNN setting informationcorresponding to an up-scaling target. The second DNN may have differentstructures based on the DNN setting information. For example, the secondDNN may include three convolution layers based on any piece of DNNsetting information, and may include four convolution layers based onanother piece of DNN setting information.

According to embodiments, the DNN setting information may only include aparameter of a filter kernel used in the second DNN. In this case, thestructure of the second DNN does not change, but only the parameter ofthe internal filter kernel may change based on the DNN settinginformation.

The AI up-scaler 234 may obtain the DNN setting information forperforming AI up-scaling on the second image 135, among the plurality ofpieces of DNN setting information. Each of the plurality of pieces ofDNN setting information used at this time is information for obtainingthe third image 145 of pre-determined resolution and/or pre-determinedquality, and is trained jointly with a first DNN.

For example, one piece of DNN setting information among the plurality ofpieces of DNN setting information may include information for obtainingthe third image 145 of resolution twice higher than resolution of thesecond image 135, for example, the third image 145 of 4 K (4096×2160)twice higher than 2 K (2048×1080) of the second image 135, and anotherpiece of DNN setting information may include information for obtainingthe third image 145 of resolution four times higher than the resolutionof the second image 135, for example, the third image 145 of 8 K(8192×4320) four times higher than 2 K (2048×1080) of the second image135.

Each of the plurality of pieces of DNN setting information is obtainedjointly with DNN setting information of the first DNN of an AI encodingapparatus 600 of FIG. 6, and the AI up-scaler 234 obtains one piece ofDNN setting information among the plurality of pieces of DNN settinginformation according to an enlargement ratio corresponding to areduction ratio of the DNN setting information of the first DNN. In thisregard, the AI up-scaler 234 may verify information of the first DNN. Inorder for the AI up-scaler 234 to verify the information of the firstDNN, the AI decoding apparatus 200 according to embodiments receives AIdata including the information of the first DNN from the AI encodingapparatus 600.

In other words, the AI up-scaler 234 may verify information targeted byDNN setting information of the first DNN used to obtain the first image115 and obtain the DNN setting information of the second DNN trainedjointly with the DNN setting information of the first DNN, by usinginformation received from the AI encoding apparatus 600.

When DNN setting information for performing the AI up-scaling on thesecond image 135 is obtained from among the plurality of pieces of DNNsetting information, input data may be processed based on the second DNNoperating according to the obtained DNN setting information.

For example, when any one piece of DNN setting information is obtained,the number of filter kernels included in each of the first, second, andthird convolution layers 310, 330, and 350 of the second DNN 300 of FIG.3, and the parameters of the filter kernels are set to values includedin the obtained DNN setting information.

Parameters of a filter kernel of 3×3 used in any one convolution layerof the second DNN of FIG. 4 are set to {1, 1, 1, 1, 1, 1, 1, 1, 1}, andwhen DNN setting information is changed afterwards, the parameters arereplaced by {2, 2, 2, 2, 2, 2, 2, 2, 2} that are parameters included inthe changed DNN setting information.

The AI up-scaler 234 may obtain the DNN setting information for AIup-scaling from among the plurality of pieces of DNN settinginformation, based on information included in the AI data, and the AIdata used to obtain the DNN setting information will now be described.

According to embodiments, the AI up-scaler 234 may obtain the DNNsetting information for AI up-scaling from among the plurality of piecesof DNN setting information, based on difference information included inthe AI data. For example, when it is verified that the resolution (forexample, 4 K (4096×2160)) of the original image 105 is twice higher thanthe resolution (for example, 2 K (2048×1080)) of the first image 115,based on the difference information, the AI up-scaler 234 may obtain theDNN setting information for increasing the resolution of the secondimage 135 two times.

According to embodiments, the AI up-scaler 234 may obtain the DNNsetting information for AI up-scaling the second image 135 from amongthe plurality of pieces of DNN setting information, based on informationrelated to the first image 115 included in the AI data. The AI up-scaler234 may pre-determine a mapping relationship between image-relatedinformation and DNN setting information, and obtain the DNN settinginformation mapped to the information related to the first image 115.

FIG. 5 is a table showing a mapping relationship between several piecesof image-related information and several pieces of DNN settinginformation.

Through embodiments according to FIG. 5, it will be determined that AIencoding and AI decoding processes according to embodiments of thedisclosure do not only consider a change of resolution. As shown in FIG.5, DNN setting information may be selected considering resolution, suchas standard definition (SD), high definition (HD), or full HD, abitrate, such as 10 Mbps, 15 Mbps, or 20 Mbps, and codec information,such as AV1, H.264, or HEVC, individually or collectively. For suchconsideration of the resolution, the bitrate and the codec information,training in consideration of each element may be jointly performed withencoding and decoding processes during an AI training process (see FIG.9).

Accordingly, when a plurality of pieces of DNN setting information areprovided based on image-related information including a codec type,resolution of an image, and the like, as shown in FIG. 5 according totraining, the DNN setting information for AI up-scaling the second image135 may be obtained based on the information related to the first image115 received during the AI decoding process.

In other words, the AI up-scaler 234 is capable of using DNN settinginformation according to image-related information by matching theimage-related information at the left of a table of FIG. 5 and the DNNsetting information at the right of the table.

As shown in FIG. 5, when it is verified, from the information related tothe first image 115, that the resolution of the first image 115 is SD, abitrate of image data obtained as a result of performing first encodingon the first image 115 is 10 Mbps, and the first encoding is performedon the first image 115 via AV1 codec, the AI up-scaler 234 may use A DNNsetting information among the plurality of pieces of DNN settinginformation.

Also, when it is verified, from the information related to the firstimage 115, that the resolution of the first image 115 is HD, the bitrateof the image data obtained as the result of performing the firstencoding is 15 Mbps, and the first encoding is performed via H.264codec, the AI up-scaler 234 may use B DNN setting information among theplurality of pieces of DNN setting information.

Also, when it is verified, from the information related to the firstimage 115, that the resolution of the first image 115 is full HD, thebitrate of the image data obtained as the result of performing the firstencoding is 20 Mbps, and the first encoding is performed via HEVC codec,the AI up-scaler 234 may use C DNN setting information among theplurality of pieces of DNN setting information, and when it is verifiedthat the resolution of the first image 115 is full HD, the bitrate ofthe image data obtained as the result of performing the first encodingis 15 Mbps, and the first encoding is performed via HEVC codec, the AIup-scaler 234 may use D DNN setting information among the plurality ofpieces of DNN setting information. One of the C DNN setting informationand the D DNN setting information is selected based on whether thebitrate of the image data obtained as the result of performing the firstencoding on the first image 115 is 20 Mbps or 15 Mbps. The differentbitrates of the image data, obtained when the first encoding isperformed on the first image 115 of the same resolution via the samecodec, indicates different qualities of reconstructed images.Accordingly, a first DNN and a second DNN may be jointly trained basedon an image quality, and accordingly, the AI up-scaler 234 may obtainDNN setting information according to a bitrate of image data indicatingthe quality of the second image 135.

According to embodiments, the AI up-scaler 234 may obtain the DNNsetting information for performing AI up-scaling on the second image 135from among the plurality of pieces of DNN setting informationconsidering both information (prediction mode information, motioninformation, quantization parameter information, and the like) providedfrom the first decoder 232 and the information related to the firstimage 115 included in the AI data. For example, the AI up-scaler 234 mayreceive quantization parameter information used during a first encodingprocess of the first image 115 from the first decoder 232, verify abitrate of image data obtained as an encoding result of the first image115 from AI data, and obtain DNN setting information corresponding tothe quantization parameter information and the bitrate. Even when thebitrates are the same, the quality of reconstructed images may varyaccording to the complexity of an image. A bitrate is a valuerepresenting the entire first image 115 on which first encoding isperformed, and the quality of each frame may vary even within the firstimage 115. Accordingly, DNN setting information more suitable for thesecond image 135 may be obtained when prediction mode information,motion information, and/or a quantization parameter obtainable for eachframe from the first decoder 232 are/is considered together, compared towhen only the AI data is used.

Also, according to embodiments, the AI data may include an identifier ofmutually agreed DNN setting information. An identifier of DNN settinginformation is information for distinguishing a pair of pieces of DNNsetting information jointly trained between the first DNN and the secondDNN, such that AI up-scaling is performed on the second image 135 to theup-scaling target corresponding to the down-scaling target of the firstDNN. The AI up-scaler 234 may perform AI up-scaling on the second image135 by using the DNN setting information corresponding to the identifierof the DNN setting information, after obtaining the identifier of theDNN setting information included in the AI data. For example,identifiers indicating each of the plurality of DNN setting informationsettable in the first DNN and identifiers indicating each of theplurality of DNN setting information settable in the second DNN may bepreviously designated. In this case, the same identifier may bedesignated for a pair of DNN setting information settable in each of thefirst DNN and the second DNN. The AI data may include an identifier ofDNN setting information set in the first DNN for AI down-scaling of theoriginal image 105. The AI up-scaler 234 that receives the AI data mayperform AI up-scaling on the second image 135 by using the DNN settinginformation indicated by the identifier included in the AI data amongthe plurality of DNN setting information.

Also, according to embodiments, the AI data may include the DNN settinginformation. The AI up-scaler 234 may perform AI up-scaling on thesecond image 135 by using the DNN setting information after obtainingthe DNN setting information included in the AI data.

According to embodiments, when pieces of information (for example, thenumber of convolution layers, the number of filter kernels for eachconvolution layer, a parameter of each filter kernel, and the like)constituting the DNN setting information are stored in a form of alookup table, the AI up-scaler 234 may obtain the DNN settinginformation by combining some values selected from values in the lookuptable, based on information included in the AI data, and perform AIup-scaling on the second image 135 by using the obtained DNN settinginformation.

According to embodiments, when a structure of DNN corresponding to theup-scaling target is determined, the AI up-scaler 234 may obtain the DNNsetting information, for example, parameters of a filter kernel,corresponding to the determined structure of DNN.

The AI up-scaler 234 obtains the DNN setting information of the secondDNN through the AI data including information related to the first DNN,and performs AI up-scaling on the second image 135 through the secondDNN set based on the obtained DNN setting information, and in this case,memory usage and throughput may be reduced compared to when features ofthe second image 135 are directly analyzed for up-scaling.

According to embodiments, when the second image 135 includes a pluralityof frames, the AI up-scaler 234 may independently obtain DNN settinginformation for a certain number of frames, or may obtain common DNNsetting information for entire frames.

FIG. 6 is a diagram showing the second image 135 including a pluralityof frames.

As shown in FIG. 6, the second image 135 may include frames t0 throughtn.

According to embodiments, the AI up-scaler 234 may obtain DNN settinginformation of a second DNN through AI data, and perform AI up-scalingon the frames t0 through tn based on the obtained DNN settinginformation. In other words, the frames t0 through tn may be processedvia AI up-scaling based on common DNN setting information.

According to embodiments, the AI up-scaler 234 may perform AI up-scalingon some of the frames t0 through tn, for example, the frames t0 throughta, by using ‘A’ DNN setting information obtained from AI data, andperform AI up-scaling on the frames ta+1 through tb by using ‘B’ DNNsetting information obtained from the AI data. Also, the AI up-scaler234 may perform AI up-scaling on the frames tb+1 through tn by using ‘C’DNN setting information obtained from the AI data. In other words, theAI up-scaler 234 may independently obtain DNN setting information foreach group including a number of frames among the plurality of frames,and perform AI up-scaling on frames included in each group by using theindependently obtained DNN setting information.

According to embodiments, the AI up-scaler 234 may independently obtainDNN setting information for each frame forming the second image 135. Inother words, when the second image 135 includes three frames, the AIup-scaler 234 may perform AI up-scaling on a first frame by using DNNsetting information obtained in relation to the first frame, perform AIup-scaling on a second frame by using DNN setting information obtainedin relation to the second frame, and perform AI up-scaling on a thirdframe by using DNN setting information obtained in relation to the thirdframe. DNN setting information may be independently obtained for eachframe included in the second image 135, according to a method ofobtaining DNN setting information based on information (prediction modeinformation, motion information, quantization parameter information, orthe like) provided from the first decoder 232 and information related tothe first image 115 included in the AI data described above. This isbecause the mode information, the quantization parameter information, orthe like may be determined independently for each frame included in thesecond image 135.

According to embodiments, the AI data may include information about towhich frame DNN setting information obtained based on the AI data isvalid. For example, when the AI data includes information indicatingthat DNN setting information is valid up to the frame ta, the AIup-scaler 234 performs AI up-scaling on the frames t0 through ta byusing DNN setting information obtained based on the AI data. Also, whenanother piece of AI data includes information indicating that DNNsetting information is valid up to the frame tn, the AI up-scaler 234performs AI up-scaling on the frames ta+1 through tn by using DNNsetting information obtained based on the other piece of AI data.

Hereinafter, the AI encoding apparatus 600 for performing AI encoding onthe original image 105 will be described with reference to FIG. 7.

FIG. 7 is a block diagram of a configuration of the AI encodingapparatus 600 according to embodiments.

Referring to FIG. 7, the AI encoding apparatus 600 may include an AIencoder 610 and a transmitter 630. The AI encoder 610 may include an AIdown-scaler 612 and a first encoder 614. The transmitter 630 may includea data processor 632 and a communication interface 634.

In FIG. 7, the AI encoder 610 and the transmitter 630 are illustrated asseparate devices, but the AI encoder 610 and the transmitter 630 may beimplemented through one processor. In this case, the AI encoder 610 andthe transmitter 630 may be implemented through an dedicated processor orthrough a combination of software and general-purpose processor such asAP, CPU or graphics processing unit GPU. The dedicated processor may beimplemented by including a memory for implementing embodiments of thedisclosure or by including a memory processor for using an externalmemory.

Also, the AI encoder 610 and the transmitter 630 may be configured by aplurality of processors. In this case, the AI encoder 610 and thetransmitter 630 may be implemented through a combination of dedicatedprocessors or through a combination of software and a plurality ofgeneral-purpose processors such as AP, CPU or GPU. The AI down-scaler612 and the first encoder 614 may be implemented through differentprocessors.

The AI encoder 610 performs AI down-scaling on the original image 105and first encoding on the first image 115, and transmits AI data andimage data to the transmitter 630. The transmitter 630 transmits the AIdata and the image data to the AI decoding apparatus 200.

The image data includes data obtained as a result of performing thefirst encoding on the first image 115. The image data may include dataobtained based on pixel values in the first image 115, for example,residual data that is a difference between the first image 115 andprediction data of the first image 115. Also, the image data includesinformation used during a first encoding process of the first image 115.For example, the image data may include prediction mode information,motion information, quantization parameter information used to performthe first encoding on the first image 115, and the like.

The AI data includes information enabling AI up-scaling to be performedon the second image 135 to an up-scaling target corresponding to adown-scaling target of a first DNN. According to embodiments, the AIdata may include difference information between the original image 105and the first image 115. Also, the AI data may include informationrelated to the first image 115. The information related to the firstimage 115 may include information about any one or any combination ofresolution of the first image 115, a bitrate of the image data obtainedas the result of performing the first encoding on the first image 115,and a codec type used during the first encoding of the first image 115.

According to embodiments, the AI data may include an identifier ofmutually agreed DNN setting information such that the AI up-scaling isperformed on the second image 135 to the up-scaling target correspondingto the down-scaling target of the first DNN.

Also, according to embodiments, the AI data may include DNN settinginformation settable in a second DNN.

The AI down-scaler 612 may obtain the first image 115 obtained byperforming the AI down-scaling on the original image 105 through thefirst DNN. The AI down-scaler 612 may determine the down-scaling targetof the original image 105, based on a pre-determined standard.

In order to obtain the first image 115 matching the down-scaling target,the AI down-scaler 612 may store a plurality of pieces of DNN settinginformation settable in the first DNN. The AI down-scaler 612 obtainsDNN setting information corresponding to the down-scaling target fromamong the plurality of pieces of DNN setting information, and performsthe AI down-scaling on the original image 105 through the first DNN setin the obtained DNN setting information.

Each of the plurality of pieces of DNN setting information may betrained to obtain the first image 115 of pre-determined resolutionand/or pre-determined quality. For example, any one piece of DNN settinginformation among the plurality of pieces of DNN setting information mayinclude information for obtaining the first image 115 of resolution halfresolution of the original image 105, for example, the first image 115of 2 K (2048×1080) half 4 K (4096×2160) of the original image 105, andanother piece of DNN setting information may include information forobtaining the first image 115 of resolution quarter resolution of theoriginal image 105, for example, the first image 115 of 2 K (2048×1080)quarter 8 K (8192×4320) of the original image 105.

According to embodiments, when pieces of information (for example, thenumber of convolution layers, the number of filter kernels for eachconvolution layer, a parameter of each filter kernel, and the like)constituting the DNN setting information are stored in a form of alookup table, the AI down-scaler 612 may obtain the DNN settinginformation by combining some values selected from values in the lookuptable, based on the down-scaling target, and perform AI down-scaling onthe original image 105 by using the obtained DNN setting information.

According to embodiments, the AI down-scaler 612 may determine astructure of DNN corresponding to the down-scaling target, and obtainDNN setting information corresponding to the determined structure ofDNN, for example, obtain parameters of a filter kernel.

The plurality of pieces of DNN setting information for performing the AIdown-scaling on the original image 105 may have an optimized value asthe first DNN and the second DNN are jointly trained. Here, each pieceof DNN setting information includes any one or any combination of thenumber of convolution layers included in the first DNN, the number offilter kernels for each convolution layer, and a parameter of eachfilter kernel.

The AI down-scaler 612 may set the first DNN with the DNN settinginformation obtained for performing the AI down-scaling on the originalimage 105 to obtain the first image 115 of a certain resolution and/or acertain quality through the first DNN. When the DNN setting informationfor performing the AI down-scaling on the original image 105 is obtainedfrom the plurality of pieces of DNN setting information, each layer inthe first DNN may process input data based on information included inthe DNN setting information.

Hereinafter, a method, performed by the AI down-scaler 612, ofdetermining the down-scaling target will be described. The down-scalingtarget may indicate, for example, by how much is resolution decreasedfrom the original image 105 to obtain the first image 115.

According to embodiments, the AI down-scaler 612 may determine thedown-scaling target based on any one or any combination of a compressionratio (for example, a resolution difference between the original image105 and the first image 115, target bitrate, or the like), compressionquality (for example, type of bitrate), compression history information,and a type of the original image 105.

For example, the AI down-scaler 612 may determine the down-scalingtarget based on the compression ratio, the compression quality, or thelike, which is pre-set or input from a user.

As another example, the AI down-scaler 612 may determine thedown-scaling target by using the compression history information storedin the AI encoding apparatus 600. For example, according to thecompression history information usable by the AI encoding apparatus 600,encoding quality, a compression ratio, or the like preferred by the usermay be determined, and the down-scaling target may be determinedaccording to the encoding quality determined based on the compressionhistory information. For example, the resolution, quality, or the likeof the first image 115 may be determined according to the encodingquality that has been used most often according to the compressionhistory information.

As another example, the AI down-scaler 612 may determine thedown-scaling target based on the encoding quality that has been usedmore frequently than a certain threshold value (for example, averagequality of the encoding quality that has been used more frequently thanthe threshold value), according to the compression history information.

As another example, the AI down-scaler 612 may determine thedown-scaling target, based on the resolution, type (for example, a fileformat), or the like of the original image 105.

According to embodiments, when the original image 105 includes aplurality of frames, the AI down-scaler 612 may independently determinedown-scaling target for a certain number of frames, or may determinedown-scaling target for entire frames.

According to embodiments, the AI down-scaler 612 may divide the framesincluded in the original image 105 into a certain number of groups, andindependently determine the down-scaling target for each group. The sameor different down-scaling targets may be determined for each group. Thenumber of frames included in the groups may be the same or differentaccording to the each group.

According to embodiments, the AI down-scaler 612 may independentlydetermine a down-scaling target for each frame included in the originalimage 105. The same or different down-scaling targets may be determinedfor each frame.

Hereinafter, an example of a structure of a first DNN 700 on which AIdown-scaling is based will be described.

FIG. 8 is a diagram showing the first DNN 700 for performing AIdown-scaling on the original image 105.

As shown in FIG. 8, the original image 105 is input to a firstconvolution layer 710. The first convolution layer 710 performs aconvolution process on the original image 105 by using 32 filter kernelshaving a size of 5×5. 32 feature maps generated as a result of theconvolution process are input to a first activation layer 720. The firstactivation layer 720 may assign a non-linear feature to the 32 featuremaps.

The first activation layer 720 determines whether to transmit samplevalues of the feature maps output from the first convolution layer 710to a second convolution layer 730. For example, some of the samplevalues of the feature maps are activated by the first activation layer720 and transmitted to the second convolution layer 730, and some of thesample values are deactivated by the first activation layer 720 and nottransmitted to the second convolution layer 730. Information representedby the feature maps output from the first convolution layer 710 isemphasized by the first activation layer 720.

An output 725 of the first activation layer 720 is input to a secondconvolution layer 730. The second convolution layer 730 performs aconvolution process on input data by using 32 filter kernels having asize of 5×5. 32 feature maps output as a result of the convolutionprocess are input to a second activation layer 740, and the secondactivation layer 740 may assign a non-linear feature to the 32 featuremaps.

An output 745 of the second activation layer 740 is input to a thirdconvolution layer 750. The third convolution layer 750 performs aconvolution process on input data by using one filter kernel having asize of 5×5. As a result of the convolution process, one image may beoutput from the third convolution layer 750. The third convolution layer750 generates one output by using the one filter kernel as a layer foroutputting a final image. According to embodiments of the disclosure,the third convolution layer 750 may output the first image 115 as aresult of a convolution operation.

There may be a plurality of pieces of DNN setting information indicatingthe numbers of filter kernels of the first, second, and thirdconvolution layers 710, 730, and 750 of the first DNN 700, a parameterof each filter kernel of the first, second, and third convolution layers710, 730, and 750 of the first DNN 700, and the like, and the pluralityof pieces of DNN setting information may be connected to a plurality ofpieces of DNN setting information of a second DNN. The connectionbetween the plurality of pieces of DNN setting information of the firstDNN and the plurality of pieces of DNN setting information of the secondDNN may be realized via joint training of the first DNN and the secondDNN.

In FIG. 8, the first DNN 700 includes three convolution layers (thefirst, second, and third convolution layers 710, 730, and 750) and twoactivation layers (the first and second activation layers 720 and 740),but this is only an example, and the numbers of convolution layers andactivation layers may vary according to embodiments. Also, according toembodiments, the first DNN 700 may be implemented as an RNN. In thiscase, a CNN structure of the first DNN 700 according to embodiments ofthe disclosure is changed to an RNN structure.

According to embodiments, the AI down-scaler 612 may include at leastone ALU for the convolution operation and the operation of theactivation layer described above. The ALU may be implemented as aprocessor. For the convolution operation, the ALU may include amultiplier that performs multiplication between sample values of theoriginal image 105 or the feature map output from previous layer andsample values of the filter kernel, and an adder that adds result valuesof the multiplication. Also, for the operation of the activation layer,the ALU may include a multiplier that multiplies an input sample valueby a weight used in a pre-determined sigmoid function, a Tan h function,or an ReLU function, and a comparator that compares a multiplicationresult and a certain value to determine whether to transmit the inputsample value to a next layer.

Referring back to FIG. 7, upon receiving the first image 115 from the AIdown-scaler 612, the first encoder 614 may reduce an information amountof the first image 115 by performing first encoding on the first image115. The image data corresponding to the first image 115 may be obtainedas a result of performing the first encoding by the first encoder 614.

The data processor 632 processes either one or both of the AI data orthe image data to be transmitted in a certain form. For example, whenthe AI data and the image data are to be transmitted in a form of abitstream, the data processor 632 may process the AI data to beexpressed in a form of a bitstream, and transmit the image data and theAI data in a form of one bitstream through the communication interface634. As another example, the data processor 632 may process the AI datato be expressed in a form of bitstream, and transmit each of a bitstreamcorresponding to the AI data and a bitstream corresponding to the imagedata through the communication interface 634. As another example, thedata processor 632 may process the AI data to be expressed in a form ofa frame or packet, and transmit the image data in a form of a bitstreamand the AI data in a form of a frame or packet through the communicationinterface 634.

The communication interface 634 transmits AI encoding data obtained as aresult of performing AI encoding, through a network. The AI encodingdata obtained as the result of performing AI encoding includes the imagedata and the AI data. The image data and the AI data may be transmittedthrough a same type of network or different types of networks.

According to embodiments, the AI encoding data obtained as a result ofprocesses of the data processor 632 may be stored in a data storagemedium including a magnetic medium such as a hard disk, a floppy disk,or a magnetic tape, an optical recording medium such as CD-ROM or DVD,or a magneto-optical medium such as a floptical disk.

Hereinafter, a method of jointly training the first DNN 700 and thesecond DNN 300 will be described with reference to FIG. 9.

FIG. 9 is a diagram for describing a method of training the first DNN700 and the second DNN 300.

In embodiments, the original image 105 on which AI encoding is performedis reconstructed to the third image 145 via an AI decoding process, andto maintain similarity between the original image 105 and the thirdimage 145 obtained as a result of AI decoding, connectivity isestablished between the AI encoding process and the AI decoding process.In other words, information lost in the AI encoding process isreconstructed during the AI decoding process, and in this regard, thefirst DNN 700 and the second DNN 300 are jointly trained.

For accurate AI decoding, ultimately, quality loss information 830corresponding to a result of comparing a third training image 804 and anoriginal training image 801 shown in FIG. 9 may be reduced. Accordingly,the quality loss information 830 is used to train both of the first DNN700 and the second DNN 300.

First, a training process shown in FIG. 9 will be described.

In FIG. 9, the original training image 801 is an image on which AIdown-scaling is to be performed and a first training image 802 is animage obtained by performing AI down-scaling on the original trainingimage 801. Also, the third training image 804 is an image obtained byperforming AI up-scaling on the first training image 802.

The original training image 801 includes a still image or a moving imageincluding a plurality of frames. According to embodiments, the originaltraining image 801 may include a luminance image extracted from thestill image or the moving image including the plurality of frames. Also,according to embodiments, the original training image 801 may include apatch image extracted from the still image or the moving image includingthe plurality of frames. When the original training image 801 includesthe plurality of frames, the first training image 802, the secondtraining image, and the third training image 804 also each include aplurality of frames. When the plurality of frames of the originaltraining image 801 are sequentially input to the first DNN 700, theplurality of frames of the first training image 802, the second trainingimage and the third training image 804 may be sequentially obtainedthrough the first DNN 700 and the second DNN 300.

For joint training of the first DNN 700 and the second DNN 300, theoriginal training image 801 is input to the first DNN 700. The originaltraining image 801 input to the first DNN 700 is output as the firsttraining image 802 via the AI down-scaling, and the first training image802 is input to the second DNN 300. The third training image 804 isoutput as a result of performing the AI up-scaling on the first trainingimage 802.

Referring to FIG. 9, the first training image 802 is input to the secondDNN 300, and according to embodiments, a second training image obtainedas first encoding and first decoding are performed on the first trainingimage 802 may be input to the second DNN 300. In order to input thesecond training image to the second DNN 300, any one codec among MPEG-2,H.264, MPEG-4, HEVC, VC-1, VP8, VP9, and AV1 may be used. Any one codecamong MPEG-2, H.264, MPEG-4, HEVC, VC-1, VP8, VP9, and AV1 may be usedto perform first encoding on the first training image 802 and firstdecoding on image data corresponding to the first training image 802.

Referring to FIG. 9, separate from the first training image 802 beingoutput through the first DNN 700, a reduced training image 803 obtainedby performing legacy down-scaling on the original training image 801 isobtained. Here, the legacy down-scaling may include any one or anycombination of bilinear scaling, bicubic scaling, lanczos scaling, orstair step scaling.

To prevent a structural feature of the first image 115 from deviatinggreatly from a structural feature of the original image 105, the reducedtraining image 803 is obtained to preserve the structural feature of theoriginal training image 801.

Before training is performed, the first DNN 700 and the second DNN 300may be set to pre-determined DNN setting information. When the trainingis performed, structural loss information 810, complexity lossinformation 820, and the quality loss information 830 may be determined.

The structural loss information 810 may be determined based on a resultof comparing the reduced training image 803 and the first training image802. For example, the structural loss information 810 may correspond toa difference between structural information of the reduced trainingimage 803 and structural information of the first training image 802.Structural information may include various features extractable from animage, such as luminance, contrast, histogram, or the like of the image.The structural loss information 810 indicates how much structuralinformation of the original training image 801 is maintained in thefirst training image 802. When the structural loss information 810 issmall, the structural information of the first training image 802 issimilar to the structural information of the original training image801.

The complexity loss information 820 may be determined based on spatialcomplexity of the first training image 802. For example, a totalvariance value of the first training image 802 may be used as thespatial complexity. The complexity loss information 820 is related to abitrate of image data obtained by performing first encoding on the firsttraining image 802. It is defined that the bitrate of the image data islow when the complexity loss information 820 is small.

The quality loss information 830 may be determined based on a result ofcomparing the original training image 801 and the third training image804. The quality loss information 830 may include any one or anycombination of an L1-norm value, an L2-norm value, an StructuralSimilarity (SSIM) value, a Peak Signal-To-Noise Ratio-Human VisionSystem (PSNR-HVS) value, an Multiscale SSIM(MS-SSIM) value, a VarianceInflation Factor (VIF) value, and a Video Multimethod Assessment Fusion(VMAF) value regarding the difference between the original trainingimage 801 and the third training image 804. The quality loss information830 indicates how similar the third training image 804 is to theoriginal training image 801. The third training image 804 is moresimilar to the original training image 801 when the quality lossinformation 830 is small.

Referring to FIG. 9, the structural loss information 810, the complexityloss information 820 and the quality loss information 830 are used totrain the first DNN 700, and the quality loss information 830 is used totrain the second DNN 300. In other words, the quality loss information830 is used to train both the first and second DNNs 700 and 300.

The first DNN 700 may update a parameter such that final lossinformation determined based on the structural loss information 810, thecomplexity loss information 820, and the quality loss information 830 isreduced or minimized. Also, the second DNN 300 may update a parametersuch that the quality loss information 830 is reduced or minimized.

The final loss information for training the first DNN 700 and the secondDNN 300 may be determined as Equation 1 below.

LossDS=a×Structural loss information+b×Complexity lossinformation+c×Quality loss information

LossUS=d×Quality loss information  [Equation 1]

In Equation 1, LossDS indicates final loss information to be reduced orminimized to train the first DNN 700, and LossUS indicates final lossinformation to be reduced or minimized to train the second DNN 300.Also, a, b, c and d may be predetermined weights.

In other words, the first DNN 700 updates parameters in a directionLossDS of Equation 1 is reduced, and the second DNN 300 updatesparameters in a direction LossUS is reduced. When the parameters of thefirst DNN 700 are updated according to LossDS derived during thetraining, the first training image 802 obtained based on the updatedparameters becomes different from a previous first training image 802obtained based on not updated parameters, and accordingly, the thirdtraining image 804 also becomes different from a previous third trainingimage 804. When the third training image 804 becomes different from theprevious third training image 804, the quality loss information 830 isalso newly determined, and the second DNN 300 updates the parametersaccordingly. When the quality loss information 830 is newly determined,LossDS is also newly determined, and the first DNN 700 updates theparameters according to newly determined LossDS. In other words,updating of the parameters of the first DNN 700 leads to updating of theparameters of the second DNN 300, and updating of the parameters of thesecond DNN 300 leads to updating of the parameters of the first DNN 700.In other words, because the first DNN 700 and the second DNN 300 arejointly trained by sharing the quality loss information 830, theparameters of the first DNN 700 and the parameters of the second DNN 300may be jointly optimized.

Referring to Equation 1, it is verified that LossUS is determinedaccording to the quality loss information 830, but this is only anexample and LossUS may be determined based on any one or any combinationof the structural loss information 810 and the complexity lossinformation 820, and the quality loss information 830.

Hereinabove, it has been described that the AI up-scaler 234 of the AIdecoding apparatus 200 and the AI down-scaler 612 of the AI encodingapparatus 600 store the plurality of pieces of DNN setting information,and methods of training each of the plurality of pieces of DNN settinginformation stored in the AI up-scaler 234 and the AI down-scaler 612will now be described.

As described with reference to Equation 1, the first DNN 700 updates theparameters based on the similarity (the structural loss information 810)between the structural information of the first training image 802 andthe structural information of the original training image 801, thebitrate (the complexity loss information 820) of the image data obtainedas a result of performing first encoding on the first training image802, and the difference (the quality loss information 830) between thethird training image 804 and the original training image 801.

The parameters of the first DNN 700 may be updated such that the firsttraining image 802 having similar structural information as the originaltraining image 801 is obtained and the image data having a small bitrateis obtained when first encoding is performed on the first training image802, and at the same time, the second DNN 300 performing AI up-scalingon the first training image 802 obtains the third training image 804similar to the original training image 801.

A direction in which the parameters of the first DNN 700 are optimizedmay vary by adjusting the weights a, b, and c of Equation 1. Forexample, when the weight b is determined to be high, the parameters ofthe first DNN 700 may be updated by prioritizing a low bitrate over highquality of the third training image 804. Also, when the weight c isdetermined to be high, the parameters of the first DNN 700 may beupdated by prioritizing high quality of the third training image 804over a high bitrate or maintaining of the structural information of theoriginal training image 801.

Also, the direction in which the parameters of the first DNN 700 areoptimized may vary according to a type of codec used to perform firstencoding on the first training image 802. This is because the secondtraining image to be input to the second DNN 300 may vary according tothe type of codec.

In other words, the parameters of the first DNN 700 and the parametersof the second DNN 300 may be jointly updated based on the weights a, b,and c, and the type of codec for performing first encoding on the firsttraining image 802. Accordingly, when the first DNN 700 and the secondDNN 300 are trained after determining the weights a, b, and c each to acertain value and determining the type of codec to a certain type, theparameters of the first DNN 700 and the parameters of the second DNN 300connected and optimized to each other may be determined.

Also, when the first DNN 700 and the second DNN 300 are trained afterchanging the weights a, b, and c, and the type of codec, the parametersof the first DNN 700 and the parameters of the second DNN 300 connectedand optimized to each other may be determined. In other words, theplurality of pieces of DNN setting information jointly trained with eachother may be determined in the first DNN 700 and the second DNN 300 whenthe first DNN 700 and the second DNN 300 are trained while changingvalues of the weights a, b, and c, and the type of codec.

As described above with reference to FIG. 5, the plurality of pieces ofDNN setting information of the first DNN 700 and the second DNN 300 maybe mapped to the information related to the first image. To set such amapping relationship, first encoding may be performed on the firsttraining image 802 output from the first DNN 700 via a certain codecaccording to a certain bitrate and the second training image obtained byperforming first decoding on a bitstream obtained as a result ofperforming the first encoding may be input to the second DNN 300. Inother words, by training the first DNN 700 and the second DNN 300 aftersetting an environment such that the first encoding is performed on thefirst training image 802 of a certain resolution via the certain codecaccording to the certain bitrate, a DNN setting information pair mappedto the resolution of the first training image 802, a type of the codecused to perform the first encoding on the first training image 802, andthe bitrate of the bitstream obtained as a result of performing thefirst encoding on the first training image 802 may be determined. Byvariously changing the resolution of the first training image 802, thetype of codec used to perform the first encoding on the first trainingimage 802 and the bitrate of the bitstream obtained according to thefirst encoding of the first training image 802, the mappingrelationships between the plurality of DNN setting information of thefirst DNN 700 and the second DNN 300 and the pieces of informationrelated to the first image may be determined.

FIG. 10 is a diagram for describing training processes of the first DNN700 and the second DNN by a training apparatus 1000.

The training of the first DNN 700 and the second DNN 300 described withreference FIG. 9 may be performed by the training apparatus 1000. Thetraining apparatus 1000 includes the first DNN 700 and the second DNN300. The training apparatus 1000 may be, for example, the AI encodingapparatus 600 or a separate server. The DNN setting information of thesecond DNN 300 obtained as the training result is stored in the AIdecoding apparatus 200.

Referring to FIG. 10, the training apparatus 1000 initially sets the DNNsetting information of the first DNN 700 and the second DNN 300, inoperations S840 and S845. Accordingly, the first DNN 700 and the secondDNN 300 may operate according to pre-determined DNN setting information.The DNN setting information may include information about any one or anycombination of the number of convolution layers included in the firstDNN 700 and the second DNN 300, the number of filter kernels for eachconvolution layer, the size of a filter kernel for each convolutionlayer, or a parameter of each filter kernel.

The training apparatus 1000 inputs the original training image 801 intothe first DNN 700, in operation S850. The original training image 801may include a still image or at least one frame included in a movingimage.

The first DNN 700 processes the original training image 801 according tothe initially set DNN setting information and outputs the first trainingimage 802 obtained by performing AI down-scaling on the originaltraining image 801, in operation S855. In FIG. 10, the first trainingimage 802 output from the first DNN 700 is directly input to the secondDNN 300, but the first training image 802 output from the first DNN 700may be input to the second DNN 300 by the training apparatus 1000. Also,the training apparatus 1000 may perform first encoding and firstdecoding on the first training image 802 via a certain codec, and theninput the second training image to the second DNN 300.

The second DNN 300 processes the first training image 802 or the secondtraining image according to the initially set DNN setting informationand outputs the third training image 804 obtained by performing AIup-scaling on the first training image 802 or the second training image,in operation S860.

The training apparatus 1000 calculates the complexity loss information820, based on the first training image 802, in operation S865.

The training apparatus 1000 calculates the structural loss information810 by comparing the reduced training image 803 and the first trainingimage 802, in operation S870.

The training apparatus 1000 calculates the quality loss information 830by comparing the original training image 801 and the third trainingimage 804, in operation S875.

The initially set DNN setting information is updated in operation S880via a back propagation process based on the final loss information. Thetraining apparatus 1000 may calculate the final loss information fortraining the first DNN 700, based on the complexity loss information820, the structural loss information 810, and the quality lossinformation 830.

The second DNN 300 updates the initially set DNN setting information inoperation S885 via a back propagation process based on the quality lossinformation 830 or the final loss information. The training apparatus1000 may calculate the final loss information for training the secondDNN 300, based on the quality loss information 830.

Then, the training apparatus 1000, the first DNN 700, and the second DNN300 may repeat operations S850 through S885 until the final lossinformation is minimized to update the DNN setting information. At thistime, during each repetition, the first DNN 700 and the second DNN 300operate according to the DNN setting information updated in the previousoperation.

Table 1 below shows effects when AI encoding and AI decoding areperformed on the original image 105 according to embodiments of thedisclosure and when encoding and decoding are performed on the originalimage 105 via HEVC.

TABLE 1 Information Subjective Image Amount (Bitrate) Quality Score(Mbps) (VMAF) Frame Al Encoding/ Al Encoding/ Content Resolution NumberHEVC Al Decoding HEVC Al Decoding Content_01 8K 300 frames 46.3 21.494.80 93.54 Content_02 (7680 × 4320) 46.3 21.6 98.05 98.98 Content_0346.3 22.7 96.08 96.00 Content_04 46.1 22.1 86.26 92.00 Content_05 45.422.7 93,42 92.98 Content_06 46.3 23.0 95.99 95.61 Average 46.11 22.2594.10 94.85

As shown in Table 1, despite subjective image quality when AI encodingand AI decoding are performed on content including 300 frames of 8 Kresolution, according to embodiments of the disclosure, is higher thansubjective image quality when encoding and decoding are performed viaHEVC, a bitrate is reduced by at least 50%.

FIG. 11 is a diagram of a first apparatus 20 for performing AIdown-scaling on the original image 105 and a second apparatus 40 forperforming AI up-scaling on the second image 135.

The first apparatus 20 receives the original image 105 and providesimage data 25 and AI data 30 to the second apparatus 40 by using an AIdown-scaler 1124 and a transformation-based encoder 1126. According toembodiments, the image data 25 corresponds to the image data of FIG. 1and the AI data 30 corresponds to the AI data of FIG. 1. Also, accordingto embodiments, the transformation-based encoder 1126 corresponds to thefirst encoder 614 of FIG. 7 and the AI down-scaler 1124 corresponds tothe AI down-scaler 612 of FIG. 7.

The second apparatus 40 receives the AI data 30 and the image data 25and obtains the third image 145 by using a transformation-based decoder1146 and an AI up-scaler 1144. According to embodiments, thetransformation-based decoder 1146 corresponds to the first decoder 232of FIG. 2 and the AI up-scaler 1144 corresponds to the AI up-scaler 234of FIG. 2.

According to embodiments, the first apparatus 20 includes a CPU, amemory, and a computer program including instructions. The computerprogram is stored in the memory. According to embodiments, the firstapparatus 20 performs functions to be described with reference to FIG.11 according to execution of the computer program by the CPU. Accordingto embodiments, the functions to be described with reference to FIG. 11are performed by a dedicated hardware chip and/or the CPU.

According to embodiments, the second apparatus 40 includes a CPU, amemory, and a computer program including instructions. The computerprogram is stored in the memory. According to embodiments, the secondapparatus 40 performs functions to be described with reference to FIG.11 according to execution of the computer program by the CPU. Accordingto embodiments, the functions to be described with reference to FIG. 11are performed by a dedicated hardware chip and/or the CPU.

In FIG. 11, a configuration controller 1122 of the first apparatus 20receives at least one input value 10. According to embodiments, the atleast one input value 10 may include any one or any combination of atarget resolution difference for the AI down-scaler 1124 and the AIup-scaler 1144, a bitrate of the image data 25, a bitrate type of theimage data 25 (for example, a variable bitrate type, a constant bitratetype, or an average bitrate type), and a codec type for thetransformation-based encoder 1126. The at least one input value 10 mayinclude a value pre-stored in the first apparatus 20 or a value inputfrom a user.

The configuration controller 1122 controls operations of the AIdown-scaler 1124 and the transformation-based encoder 1126, based on thereceived input value 10. According to embodiments, the configurationcontroller 1122 obtains DNN setting information for the AI down-scaler1124 according to the received input value 10, and sets the AIdown-scaler 1124 with the obtained DNN setting information. According toembodiments, the configuration controller 1122 may transmit the receivedinput value 10 to the AI down-scaler 1124 and the AI down-scaler 1124may obtain the DNN setting information for performing AI down-scaling onthe original image 105, based on the received input value 10. Accordingto embodiments, the configuration controller 1122 may provide, to the AIdown-scaler 1124, additional information, for example, color format(luminance component, chrominance component, red component, greencomponent, or blue component) information to which AI down-scaling isapplied and tone mapping information of a high dynamic range (HDR),together with the input value 10, and the AI down-scaler 1124 may obtainthe DNN setting information considering the input value 10 and theadditional information. According to embodiments, the configurationcontroller 1122 transmits at least a part of the received input value 10to the transformation-based encoder 1126 and the transformation-basedencoder 1126 performs first encoding on the first image 115 via abitrate of a certain value, a bitrate of a certain type, and a certaincodec.

The AI down-scaler 1124 receives the original image 105 and performs anoperation described with reference to any one or any combination ofFIGS. 1, 7, 8, 9, and 10 to obtain the first image 115.

According to embodiments, the AI data 30 is provided to the secondapparatus 40. The AI data 30 may include either one or both ofresolution difference information between the original image 105 and thefirst image 115, and information related to the first image 115. Theresolution difference information may be determined based on the targetresolution difference of the input value 10, and the information relatedto the first image 115 may be determined based on at least one of atarget bitrate, the bitrate type, or the codec type. According toembodiments, the AI data 30 may include parameters used during the AIup-scaling. The AI data 30 may be provided from the AI down-scaler 1124to the second apparatus 40.

The image data 25 is obtained as the original image 105 is processed bythe transformation-based encoder 1126, and is transmitted to the secondapparatus 40. The transformation-based encoder 1126 may process thefirst image 115 according to MPEG-2, H.264 AVC, MPEG-4, HEVC, VC-1, VP8,VP9, or VA1.

A configuration controller 1142 of the second apparatus 40 controls anoperation of the AI up-scaler 1144, based on the AI data 30. Accordingto embodiments, the configuration controller 1142 obtains the DNNsetting information for the AI up-scaler 1144 according to the receivedAI data 30, and sets the AI up-scaler 1144 with the obtained DNN settinginformation. According to embodiments, the configuration controller 1142may transmit the received AI data 30 to the AI up-scaler 1144 and the AIup-scaler 1144 may obtain the DNN setting information for performing AIup-scaling on the second image 135, based on the AI data 30. Accordingto embodiments, the configuration controller 1142 may provide, to the AIup-scaler 1144, additional information, for example, the color format(luminance component, chrominance component, red component, greencomponent, or blue component) information to which AI up-scaling isapplied, and the tone mapping information of HDR, together with the AIdata 30, and the AI up-scaler 1144 may obtain the DNN settinginformation considering the AI data 30 and the additional information.

According to embodiments, the AI up-scaler 1144 may receive the AI data30 from the configuration controller 1142, receive at least one ofprediction mode information, motion information, or quantizationparameter information from the transformation-based decoder 1146, andobtain the DNN setting information based on the AI data 30 and any oneor any combination of the prediction mode information, the motioninformation, and the quantization parameter information.

The transformation-based decoder 1146 may process the image data 25 toreconstruct the second image 135. The transformation-based decoder 1146may process the image data 25 according to MPEG-2, H.264 AVC, MPEG-4,HEVC, VC-1, VP8, VP9, or AV1.

The AI up-scaler 1144 may obtain the third image 145 by performing AIup-scaling on the second image 135 provided from thetransformation-based decoder 1146, based on the set DNN settinginformation.

The AI down-scaler 1124 may include a first DNN and the AI up-scaler1144 may include a second DNN, and according to embodiments, DNN settinginformation for the first DNN and second DNN are trained according tothe training method described with reference to FIGS. 9 and 10.

FIG. 12 is a diagram for describing a concept of a streaming system1200, according to embodiments of the disclosure.

Referring to FIG. 12, the streaming system 1200 may include a server1210 and a terminal 1220. However, this is an example, and elements ofthe streaming system 1200 are not limited to the server 1210 and theterminal 1220.

The server 1210 may stream image data to the terminal 1220. In thedisclosure, streaming refers to an operation of transmitting andreceiving image data between the server 1210 and the terminal 1220 suchthat the terminal 1220 may reproduce the image data in real time. Also,the server 1210 may stream various types of data including audio dataand text data as well as image data to the terminal 1220, but in thedisclosure, a method of streaming image data according to embodiments ofthe disclosure will be described.

For streaming, the server 1210 and the terminal 1220 may be connectedthrough a network 1230. The server 1210 may stream image data to theterminal 1220 via the network 1230.

For example, when the terminal 1220 requests the server 1210 forpredefined image data from among a plurality of items of image data, thepredefined image data may be transmitted to the terminal 1220. Theplurality of items of image data may be also referred to as a pluralityof versions of an image content (e.g., a movie, a television content, avideo, etc.), or a plurality of different quality versions of an imagecontent. The predefined image data may be image data that corresponds tosetting by a user. However, the disclosure is not limited to theexample, and thus, in another example of the disclosure, the predefinedimage data may be image data having a quality set as a default whenstreaming between the server 1210 and the terminal 1220 starts. Whilethe server 1210 is streaming the image data, a state of the network 1230may be changeable. Information regarding the state of the network 1230may be determined according to an amount of traffic in transmission andreception paths between the server 1210 and the terminal 1220, and thismay be described as a congestion level. However, this is an example, andthe state of the network 1230 is not described only according to thetraffic occurring in the transmission and reception paths.

To adaptively perform streaming, based on a changeable state of anetwork, the server 1210 may adjust either one or both of a bitrate anda resolution of image data that is to be transmitted from the server1210 to the terminal 1220. The server 1210 may store a plurality ofitems of image data 10 (e.g., a high-definition (HD) class IRON MANmovie, a standard-definition (SD) class IRON MAN movie, a 15-Mbps IRONMAN movie, a 10-Mbps IRON MAN movie, etc.) for same image content (e.g.,an IRON MAN movie), the plurality of items of image data 10 beingobtained from the same image content by adjusting either one or both ofa bitrate and a resolution. However, this is an example, and parametersfor adjusting a quality of image data may further include a samplingfrequency, a frame rate, a window size (e.g., 1920×1080 (1080p) HD,1280×720 (720p) HD, etc.), a video codec (e.g., H.264, H.265, AdvanceVideo Coding, etc.), a pixel aspect ratio, an audio codec (e.g., AdvanceAudio Coding), or the like.

The server 1210 according to embodiments of the disclosure may store theplurality of items of image data 10 having different qualities, and theplurality of items of image data 10 may include either one or both ofAI-encoded image data 12 or non-AI-encoded image data 14. The AI-encodedimage data 12 is generated through the aforementioned AI encodingprocess, and the AI encoding process includes a process of performingAI-downscaling on an original image through the first DNN 700. In thisregard, the first DNN 700 is trained jointly with the second DNN 300 ofthe terminal 1220, and when the terminal 1220 receives AI-encoded imagedata, the terminal 1220 may perform AI-upscaling on the image datathrough the second DNN 300. Also, the AI-encoded image data 12 may bestored together with AI data related to AI-downscaling, and the AI datamay be used in an AI-upscaling process by the terminal 1220.

The server 1210 may provide additional information of the plurality ofitems of image data 10 to the terminal 1220 so as to allow the terminal1220 to request image data that corresponds to the state of the network1230, from among the plurality of items of image data 10. The additionalinformation may include quality information and AI scale conversioninformation about each of the plurality of items of image data 10.

A quality of each of the plurality of items of image data 10 may bedetermined according to a resolution and a bitrate, and the qualityinformation may include values of a resolution and a bitrate of each ofthe plurality of items of image data 10. However, this is an example,and the quality may be determined according to a sampling frequency, aframe rate, a window size, a video codec, a pixel aspect ratio, an audiocodec, or the like. The AI scale conversion information may includeinformation indicating whether image data is AI-encoded image data, avalue of AI scale conversion level, or the like. In this regard, the AIscale conversion level is an index indicating a difference betweenAI-downscaled image data and original image data, and may be definedwith respect to a resolution, a bitrate, or the like. For example, whenresolutions of 8K, 4K, full HD (FHD), and HD are supported in thestreaming system 1200, a difference between two adjacent resolutions maybe defined as one level interval. In this case, it may be described thata level difference between 8K and 4K corresponds to one level interval,and a level difference between 8K and FHD corresponds to two levelintervals. For example, the resolutions of 8K, 4K, full HD (FHD), and HDmay be set to a first resolution level, a second resolution level, athird resolution level, and a fourth resolution level, respectively, anda level interval (also referred to as “resolution level interval”)between the resolutions of 8K, 4K, full HD (FHD), and HD may bedetermined as a difference between the resolution levels set to theresolutions of 8K, 4K, full HD (FHD), and HD. Also, when bitrates of 40Mbps, 30 Mbps, 20 Mbps, and 10 Mbps are supported in the streamingsystem 1200, a difference between two adjacent bitrates may be definedas one level interval. For example, the bitrates of 40 Mbps, 30 Mbps, 20Mbps, and 10 Mbps may be set to a first bitrate level, a second bitratelevel, a third bitrate level, and a fourth bitrate level, respectively,and a level interval (also referred to as “bitrate level interval”)between the bitrates of 40 Mbps, 30 Mbps, 20 Mbps, and 10 Mbps may bedetermined as a difference between the bitrate levels set to thebitrates of 40 Mbps, 30 Mbps, 20 Mbps, and 10 Mbps. According toembodiments of the disclosure, a level and a level interval may bedefined based on a combination of the resolution and the bitrate. Thatis, a difference between 8K & 40 Mbps and 8K and 30 Mbps may be definedas one level interval, and a difference between 8K & 30 Mbps and 4K & 20Mbps may be defined as two level intervals. However, this is an example,and the AI scale conversion level may be determined according to otherfactors not only the resolution and the bitrate.

The terminal 1220 according to embodiments of the disclosure may checkresolutions and bitrates of the plurality of items of image data 10included in the additional information, and may request the server 1210to transmit image data that has been AI encoded at a particularresolution or bitrate. Also, the terminal 1220 may determine aresolution and a bitrate of image data to be requested for the server1210, according to a state of the network 1230. For example, while theterminal 1220 receives AI-encoded image data of FHD and 5 Mbps from theserver 1210, when it is confirmed that a congestion level of a networkis improved because a bit error rate (BER) of the received image data isdecreased, the terminal 1220 may request the server 1210 for AI-encodedimage data of 4K and 10 Mbps. In embodiments of the disclosure, theterminal 1220 may request the server 1210 to increase or decrease theresolution level, the bitrate level, or the level of the combination ofthe resolution level and the bitrate level.

However, this is an example, and the terminal 1220 may requesttransmission of image data of a particular resolution or a particularbitrate, and the server 1210 may determine whether to transmitAI-encoded image data or non-AI-encoded image data. Embodiments of thedisclosure in which the terminal 1220 requests the server 1210 for imagedata based on the additional information will be further described belowwith reference to FIGS. 18 to 20.

FIG. 13A is a flowchart for describing a method of streaming data, themethod being performed by a server, according to embodiments of thedisclosure.

In operation S1310, the server 1210 may transmit, to a terminal 1220,additional information of a plurality of items of image data ofdifferent qualities.

In response to a request from the terminal 1220, the server 1210 maytransmit, to the terminal 1220, the additional information of theplurality of items of image data. The additional information may includequality information and AI scale conversion information about each ofthe plurality of items of image data. The quality information mayinclude resolutions and bitrate values of the plurality of items ofimage data, respectively, and the AI scale conversion information mayinclude information indicating whether image data is AI-encoded imagedata, a value of an AI scaling conversion level, or the like. However,this is an example, and a plurality of pieces of information included inthe additional information will be further described below withreference to FIGS. 21 to 24.

The additional information may be Media Presentation Description (MPD)according to the Moving Picture Experts Group (MPEG)-Dynamic AdaptiveStreaming over Hyper Text Transfer Protocol (HTTP) (DASH) standard.However, this is an example, and the additional information may beprovided as a different type of a manifest file stored in an ExtensibleMarkup Language (XML) format.

In operation S1320, the server 1210 may receive, from the terminal 1220,a request for image data whose quality corresponds to a state of anetwork between the terminal and the server 1210, based on theadditional information.

The server 1210 may receive, from the terminal 1220, a request messagerequesting image data of a particular quality from among the pluralityof items of image data, and the request message may include informationfor specifying the one from among the plurality of items of image data.

According to embodiments of the disclosure, the request message mayinclude quality information about the image data requested by theterminal 1220. For example, the request message may include informationabout either one or both of a bitrate or a resolution.

According to embodiments of the disclosure, the request message mayinclude quality information and information indicating whether AIdownscaling has been applied. For example, when AI-encoded image data isrequested, the quality information indicates a quality of the AI-encodedimage data. That is, when the AI-encoded image data is requested, andthe quality information thereof indicates FHD and 5 Mbps, the server1210 may determine that the terminal requests the AI-encoded image datawhose resolution is FHD and bitrate is 5 Mbps, the AI-encoded image databeing obtained as a result of performing AI downscale. According toembodiments of the disclosure, when non-AI-encoded image data isrequested, the quality information indicates a quality of original imagedata. That is, when the non-AI-encoded image data is requested, and thequality information thereof indicates FHD and 5 Mbps, the server 1210may determine that the terminal 1220 requests the original image datawhose resolution is FHD and bitrate is 5 Mbps.

The request message according to embodiments of the disclosure mayinclude capability information of the terminal 1220 and qualityinformation about image data requested by the terminal 1220. Forexample, when the quality information indicates FHD and 20 Mbps, and theterminal 1220 supports AI upscale, the server 1210 may determine thatthe terminal 1220 requests AI-encoded image data whose resolution is FHDand bitrate is 20 Mbps, the AI-encoded image data being obtained as aresult of performing AI downscaling. According to embodiments of thedisclosure, when the quality information indicates FHD and 20 Mbps, andthe terminal 1220 does not support AI upscale, the server 1210 maydetermine that the terminal 1220 requests original image data whoseresolution is FHD and bitrate is 20 Mbps.

In operation S1330, in response to the request, the server 1210 maytransmit, to the terminal 1220, AI data and the image data that has beenAI encoded through the first DNN trained jointly with the second DNN ofthe terminal 1220.

When the terminal 1220 requests the AI-encoded image data, the server1210 may transmit, to the terminal 1220, the AI-encoded image datatogether with the AI data including information that may be used toperform AI upscaling on the AI-encoded image data. For example, the AIdata may include information about any one or any combination ofinformation indicating whether AI downscaling has been applied, an AIscale conversion level, and DNN configuration information used in AIupscaling. However, this is an example, and the AI data may includeother information that may be used in performing AI upscaling.

The server 1210 may transmit the AI-encoded image data in a unit of asegment to the terminal 1220. The segment may be generated bypartitioning the AI-encoded image data, based on a preset time unit.However, this is an example, and a transmission unit of the AI-encodedimage data which is transmitted from the server 1210 is not limited tothe unit of the segment.

In operation S1340, when the state of the network between the terminal1220 and the server 1210 is changed, the server 1210 may receive, fromthe terminal 1220, a request for image data of a different qualitycorresponding to the changed state of the network.

The terminal 1220 may periodically determine a state of the network. Forexample, the terminal 1220 may periodically measure a timestamp at whichimage data is received, and a BER, and thus may determine the state ofthe network. Also, when the state of the network is changed, theterminal 1220 may change a quality of image data to be requested for theserver 1210. For example, in a case in which the terminal 1220 requestedthe server 1210 for image data of FHD and 5 Mbps at a first time pointat which the network is congested, the terminal 1220 may request theserver 1210 for image data of 4K and 10 Mbps at a second time pointafter the first time point, if the congestion is reduced and thecondition of the network is improved at the second time point. Theterminal 1220 or the server 1210 may determine that a network congestionoccurs when any one or any combination of a delay (or a latency), a biterror rate, a packet loss, and a timeout (e.g., a lost connection) isobserved. For example, when a time it takes for a destination to receivea packet sent by a sender (i.e., a delay or a latency) is longer than athreshold delay, the terminal 1220 or the server 1210 may determine thata network congestion occurs. In another example, when the terminal 1220experiences buffering longer than a threshold buffering time whilereproducing a video transmitted from the server 1210, the terminal 1220or the server 1210 may determine that a network congestion occurs.

Information included in a request message to be transmitted from theterminal 1220 to the server 1210 so as to request the image data of thedifferent quality corresponding to the changed state of the network maycorrespond to the descriptions provided with reference to S1320.

FIG. 13B is a flowchart for describing a method of streaming data, themethod being performed by a terminal 1220, according to embodiments ofthe disclosure.

In operation S1315, the terminal 1220 may request a server 1210 forimage data of a quality corresponding to a state of a network, the imagedata being from among a plurality of items of image data, based onadditional information. The additional information may include qualityinformation and AI scale conversion information about each of theplurality of items of image data stored in the server 1210. The qualityinformation may include resolutions and bitrates of the plurality ofitems of image data, respectively, and the AI scale conversioninformation may include information indicating whether image data isAI-encoded image data, and a value of a level at which AI downscalinghas been performed.

The terminal 1220 may determine the quality of the image datacorresponding to the state of the network of the terminal 1220, based onthe state of the network. The state of the network may be determinedbased on a timestamp and a BER of image data received by the terminal1220.

The timestamp refers to information indicating an elapse time from areference time to a reception time of image data. For example, in a casein which an average value of the timestamp in a first time period is 3ms whereas an average value of the timestamp in a second time period is5 ms, the terminal 1220 may determine that the state of the network inthe second time period is congested compared to the first time period.Also, the BER refers to a ratio of an error bit number to a totaltransmission bit number. For example, when the BER is lower than apreset reference, the terminal 1220 may determine that the state of thenetwork is not congested. As another example, in a case in which a valueof the BER is 0.005 in the first time period whereas a value of the BERis 0.01 in the second time period, the terminal 1220 may determine thatthe state of the network in the second time period is congested comparedto the first time period. However, this is an example, and the state ofthe network may be determined based on another information.

For example, when the terminal 1220 estimates that the state of thenetwork between the server 1210 and the terminal 1220 is congested, theterminal 1220 may select image data of 20 Mbps that is a relatively lowbitrate from among 50 Mbps, 40 Mbps, 30 Mbps, and 20 Mbps that arerespective bitrates of the plurality of items of image data. However,this is an example, and a method of determining, by the terminal 1220,the quality of the image data corresponding to the state of the networkis not limited to the example.

The terminal 1220 may transmit, to the server 1210, a request messagerequesting the image data of the determined quality. According toembodiments of the disclosure, the request message may include qualityinformation about the image data requested by the terminal 1220. Forexample, the request message may include information about at least oneof a bitrate or a resolution. The request message according toembodiments of the disclosure may include quality information andinformation indicating whether AI downscaling has been applied. Therequest message according to embodiments of the disclosure may includecapability information of the terminal 1220 and quality informationabout the image data requested by the terminal 1220. The capabilityinformation may include information about whether the terminal 1220supports AI upscaling.

In operation S1325, when the terminal 1220 receives the image datacorresponding to the request, and AI data, the terminal 1220 maydetermine whether to perform AI upscaling on the received image data,based on the AI data.

According to embodiments of the disclosure, the terminal 1220 maydetermine, based on the AI data, whether the received image data hasbeen AI encoded through the first DNN trained jointly with the secondDNN. The AI data may include information about at least one ofinformation indicating whether AI downscaling has been applied, an AIscale conversion level, or DNN configuration information used in AIupscaling. The DNN configuration information may be provided as anindicator indicating the number of convolution layers, the number offilter kernels of each convolution layer, a parameter of each filterkernel, or the like. However, this is an example, and the DNNconfiguration information may be provided as a lookup table, and asanother example, the second DNN may be provided as the DNN configurationinformation. However, this is an example, and the AI data may includeother information required for the terminal 1220 to perform AIupscaling.

According to embodiments of the disclosure, when the terminal 1220included information specifying AI-encoded image data in the requestmessage for image data in aforementioned operation S1315, the terminal1220 may determine that AI downscaling has been applied to image datathat is received in response to the request message.

In operation S1335, the terminal 1220 may perform AI upscaling on theimage data received through the second DNN trained jointly with thefirst DNN, based on a result of determining whether to perform AIupscaling.

When the terminal 1220 determines that the received image data is imagedata that has been AI encoded through the first DNN trained jointly withthe second DNN, the terminal may perform AI upscaling on the receivedimage data through the second DNN.

According to embodiments of the disclosure, the terminal 1220 maydetermine DNN configuration information of the second DNN, based on atleast one of a resolution or a bitrate of the AI-encoded image data. Forexample, when the resolution and the bitrate of the AI-encoded imagedata are 4K and 10 Mbps, the terminal 1220 may select DNN configurationinformation that is optimized to the resolution and the bitrate and isfrom among a plurality of pieces of DNN configuration information. Inthis regard, the plurality of pieces of DNN configuration informationthat are respectively optimized to the resolutions and the bitrates maybe pre-trained in the terminal 1220, and information thereof may beincluded in the AI data as will be described in embodiments below.According to embodiments of the disclosure, the terminal 1220 may obtainDNN configuration information that is optimized for performing AIupscaling on the AI-encoded image data, based on the DNN configurationinformation included in the AI data.

The terminal 1220 may perform AI upscaling on the AI-encoded image data,based on the selected DNN configuration information, through the secondDNN trained jointly with the first DNN.

In operation S1345, when a state of the network is changed, the terminal1220 may request the server 1210 for image data of a different qualitycorresponding to the changed state of the network, based on theadditional information.

For example, although the terminal 1220 requested AI-encoded image dataof FHD and 5 Mbps in aforementioned operation S1315, when an interval oftimestamps of image data received thereafter becomes short or a BER isdecreased, the terminal 1220 may determine that a congestion level ofthe network is alleviated and improved and thus may request the server1210 for AI-encoded image data of 4K and a 10-Mbps bitrate.

As another example, although the terminal 1220 requested AI-encodedimage data of 4K and 10 Mbps in aforementioned operation S1315, when aninterval of timestamps of image data received thereafter becomes long ora BER is increased, the terminal 1220 may determine that the congestionlevel of the network deteriorates and thus may request the server 1210for AI-encoded image data of FHD and 5 Mbps.

As another example, although the terminal 1220 requested AI-encodedimage data of FHD and 5 Mbps in aforementioned operation S1315, when aninterval of timestamps of image data received thereafter becomes long ora BER is increased, the terminal 1220 may determine that the congestionlevel of the network deteriorates and thus may request the server 1210for image data of HD and 1 Mbps. That is, when a resolution and abitrate are less than a predetermined reference, the terminal 1220 mayconsider a level of image data that is to be reconstructed by AIupscaling, and thus may request the server 1210 for image data on whichAI downscaling has not been performed. However, this is an example, anda method, performed by the terminal 1220, of changing a quality of imagedata based on a change in a state of a network is not limited to theaforementioned example.

Information included in a request message transmitted from the terminal1220 to the server 1210 so as to request the image data of the differentquality corresponding to the state of the network may correspond to theaforementioned descriptions provided with reference to operation S1315.

FIG. 14A is a flowchart for describing a method of streaming data, themethod being performed by a server, according to embodiments of thedisclosure.

In operation S1410, the server 1210 may receive, from a terminal 1220, arequest for one of a plurality of items of image data of differentqualities determined based on additional information of the plurality ofitems of image data.

The additional information may be provided to the terminal 1220 from aservice server that is independently separate from the server 1210.However, this is an example, and the additional information may beprovided from the server 1210 to the terminal 1220.

According to embodiments of the disclosure, the server 1210 may receive,from the terminal 1220, a request for image data whose quality is set bya user based on the additional information. For example, when the userof the terminal 1220 selects a quality of FHD and 5 Mbps, the server1210 may receive a request for image data of FHD and 5 Mbps from theterminal 1220. However, this is an example, and according to embodimentsof the disclosure, the server 1210 may receive, from the terminal 1220,a request for image data of a quality set as a default. For example, ina case in which streaming of image data starts between the terminal 1220and the server 1210, when a state of a network between the terminal 1220and the server 1210 is not confirmed, the server 1210 may receive, fromthe terminal 1220, a request for image data of a lowest quality fromamong a plurality of qualities. According to embodiments of thedisclosure, the server 1210 may receive, from the terminal 1220, arequest for image data of a particular quality (e.g., HD and 4 Mbps) setas a default. According to embodiments of the disclosure, the server1210 may receive, from the terminal 1220, a request for image data forwhich information about whether AI encoding has been performed isspecified, in addition to a quality.

In operation S1420, in response to the request, the server 1210 maytransmit, to the terminal 1220, AI data and image data that has been AIencoded through a DNN for downscaling trained jointly with a DNN forupscaling of the terminal 1220.

When the terminal 1220 requests AI-encoded image data, the server 1210may transmit, to the terminal 1220, the AI-encoded image data togetherwith the AI data including information that may be needed for upscalingthe AI-encoded image data. For example, the AI data may includeinformation about at least one of information indicating whether AIdownscaling has been applied, an AI scale conversion level, or DNNconfiguration information used in AI upscaling. However, this is anexample, and the AI data may include other information that may beneeded for the terminal 1220 to perform AI upscaling.

The server 1210 may transmit the AI-encoded image data in a unit of asegment to the terminal 1220. The segment may be generated bypartitioning the AI-encoded image data, based on a preset time unit.However, this is an example, and a transmission unit of the AI-encodedimage data which is transmitted from the server 1210 is not limited tothe unit of the segment.

In operation S1430, according to the state of the network between theterminal 1220 and the server 1210, the server 1210 may receive, from theterminal 1220, a request for image data of a different quality fromamong the plurality of items of image data, based on the additionalinformation.

According to embodiments of the disclosure, the request may includequality information about the image data requested by the terminal 1220.For example, the request may include information about at least one of abitrate or a resolution. As another example, the request may includequality information and information indicating whether AI downscalinghas been applied. As another example, a request message may includecapability information of the terminal and quality information about theimage data requested by the terminal 1220.

FIG. 14B is a flowchart for describing a method of streaming data, themethod being performed by the terminal 1220, according to embodiments ofthe disclosure.

In operation S1405, the terminal 1220 may request particular image data.The terminal 1220 according to embodiments of the disclosure may requestimage data of a particular quality. For example, the terminal 1220 mayrequest image data of FHD and 5 Mbps. However, this is an example, andaccording to embodiments of the disclosure, the terminal 1220 mayrequest a server 1210 for image data without specifying a qualitythereof.

According to embodiments of the disclosure, the terminal 1220 mayrequest a server 1210 for image data by specifying information aboutwhether AI encoding has been performed, in addition to a quality.

In operation S1415, the terminal 1220 may receive image datacorresponding to the request. The terminal 1220 according to embodimentsof the disclosure may receive additional information together with theimage data corresponding to the request. The additional information mayinclude quality information, AI scale conversion information, or thelike about a plurality of items of image data that may be provided fromthe server 1210 to the terminal 1220. However, this is an example, andthe additional information may include a plurality of pieces of otherinformation for identifying the plurality of items of image data,respectively.

Also, the additional information may include respective uniform resourcelocators (URLs) for receiving the plurality of items of image data thatare identifiable based on the quality information, the AI scaleconversion information, or the like.

Additional information that is to be received by the terminal 1220 maybe determined, according to capability information of the terminal 1220according to embodiments of the disclosure. For example, when theterminal 1220 is a device that supports AI decoding, the server 1210 maytransmit additional information including the AI scale conversioninformation to the terminal 1220, and when the terminal 1220 is a devicethat does not support AI decoding, the server 1210 may transmitadditional information not including the AI scaling conversioninformation to the terminal 1220. However, this is an example, and in acase in which the terminal 1220 is a device that does not support AIdecoding, even when the terminal 1220 receives the additionalinformation including the AI scale conversion information, the terminal1220 may not interpret but may ignore the additional information. In thepresent embodiment of the disclosure, the capability information of theterminal 1220 may have been previously provided to the server 1210, ormay be included in the request for the particular image data.

The aforementioned embodiment of the disclosure is an example, and thusthe additional information may be provided from the server 1210 to theterminal 1220 after image data is received during a predetermined periodor may be provided from the server 1210 to the terminal 1220 before theimage data is received.

In operation S1425, the terminal 1220 may determine whether a state of anetwork is changed.

The state of the network may be determined based on a timestamp and aBER of the image data received by the terminal 1220. For example, as aresult of determination based on the timestamp, when the terminal 1220determines that a time of receiving the image data from the server 1210is delayed, the terminal 1220 may determine that the state of thenetwork is congested. As another example, when the BER is less than apredetermined reference, the terminal 1220 may determine that the stateof the network is not congested. However, this is an example, and thestate of the network may be determined based on other information.

According to embodiments of the disclosure, when at least one of thetimestamp or the BER of the image data is changed, the terminal 1220 maydetermine that the state of the network has been changed, and accordingto embodiments of the disclosure, when at least one of the timestamp orthe BER exceeds a preset range, or when a difference differing from aprevious measurement value by at least a predetermined value occurs orthe difference is maintained for a certain time period, the terminal1220 may determine that the state of the network has been changed. Forexample, when a previous BER is 0.001, and a BER measured thereafter isin a range of between 0.0095 and 0.005, the terminal 1220 determinesthat the state of the network is maintained, but when the BER exceedsthe corresponding range, the terminal 1220 may determine that the stateof the network is changed. However, this is an example, and a referenceby which determination with respect to whether the state of the networkis changed is made is not limited to the aforementioned example.

As a result of the determination, when the terminal 1220 determines thatthe state of the network is not changed, the terminal 1220 may receiveimage data corresponding to the quality requested in operation S1405 orcorresponding to whether AI downscaling has been performed.

In operation S1435, the terminal 1220 may change image data to berequested, based on the additional information. In aforementionedoperation S1425, when the terminal 1220 determines that the state of thenetwork has been changed, the terminal 1220 may determine requirableimage data (or image data quality settings), based on the additionalinformation and the capability of the terminal 1220.

To determine the requirable image data (or the image data qualitysettings), the terminal 1220 may determine whether the terminal 1220 cansupport AI decoding. According to embodiments of the disclosure, theterminal 1220 may determine whether the terminal 1220 can perform AIupscaling. According to embodiments of the disclosure, the terminal 1220may determine whether AI-encoded image data of a corresponding qualitycan be a type of DNN configuration information that can be AI upscaledaccording to a type of DNN configuration information, based on thequality corresponding to a changed state of the network, the DNNconfiguration information trained in a second DNN of the terminal 1220,jointly with a first DNN of the server 1210. For example, when thequality corresponding to the changed state of the network is FHD and 5Mbps, the terminal 1220 may determine whether the second DNN has beentrained jointly with the first DNN of the server 1210 so as to AIupscale the AI-encoded image data of FHD and 5 Mbps.

According to embodiments of the disclosure, in a case of image data of asame quality, the terminal 1220 may determine whether AI downscaling hasbeen performed on the image data to be requested, an AI downscale level,a type of DNN configuration information used in the AI downscaling, orthe like, based on a hardware device specification of the terminal 1220,a type of codec, or the like. Information about whether each image datahas been AI downscaled, an AI downscale level of each image data, a typeof DNN configuration information used in the AI downscaling, or the likemay be included in the additional information and provided to theterminal 1220, and the descriptions therefor will be further providedbelow with reference to FIGS. 20 to 23.

FIG. 15A is a flowchart for describing a method of streaming data, themethod being performed by a server, according to embodiments of thedisclosure.

In operation S1510, the server 1210 may transmit, to a terminal 1220,additional information of a plurality of items of image data ofdifferent qualities.

The server 1210 may transmit the additional information of the pluralityof items of image data, in response to a request from the terminal 1220.However, this is an example, and the server 1210 may transmit theadditional information to the terminal 1220 when a communication sessionfor streaming image data is established between the server 1210 and theterminal 1220.

In the present embodiment of the disclosure, the additional informationmay correspond to that described with reference to FIG. 13A.

In operation S1520, the server 1210 may receive, from the terminal 1220,a request for image data from among the plurality of items of imagedata, based on the additional information.

According to embodiments of the disclosure, the server 1210 may receive,from the terminal 1220, a request for image data whose quality is set bya user. For example, when the user of the terminal 1220 selects aquality of FHD and 5 Mbps, the server 1210 may receive a request forimage data of FHD and 5 Mbps from the terminal 1220. However, this is anexample, and according to embodiments of the disclosure, the server 1210may receive, from the terminal 1220, a request for image data of aquality set as a default. For example, in a case in which streaming ofimage data starts between the terminal 1220 and the server 1210, when astate of a network between the terminal 1220 and the server is notconfirmed, the server 1210 may receive, from the terminal 1220, arequest for image data of a lowest quality from among a plurality ofqualities. According to embodiments of the disclosure, the server 1210may receive, from the terminal 1220, a request for image data of aparticular quality (e.g., HD and 4 Mbps) set as a default. According toembodiments of the disclosure, the server 1210 may receive, from theterminal, a request for image data for which information about whetherAI encoding has been performed is specified, in addition to a quality.

In operation S1530, in response to the request, the server 1210 maytransmit, to the terminal 1220, AI data and image data that has been AIencoded through a DNN for downscaling trained jointly with a DNN forupscaling of the terminal 1220.

When the terminal 1220 requests AI-encoded image data, the server 1210may transmit, to the terminal 1220, the AI-encoded image data togetherwith the AI data including information necessary for upscaling theAI-encoded image data. For example, the AI data may include informationabout at least one of information indicating whether AI downscaling hasbeen applied, an AI scale conversion level, or DNN configurationinformation used in AI upscaling. However, this is an example, and theAI data may include other information necessary for the terminal 1220 toperform AI upscaling.

The server 1210 may transmit the AI-encoded image data in a unit of asegment to the terminal 1220. The segment may be generated bypartitioning the AI-encoded image data, based on a preset time unit.However, this is merely an example, and a transmission unit of theAI-encoded image data which is transmitted from the server 1210 is notlimited to the unit of the segment.

In operation S1540, according to the state of the network between theterminal 1220 and the server 1210, the server 1210 may receive, from theterminal 1220, a request for image data of a different quality fromamong the plurality of items of image data, based on the additionalinformation.

According to embodiments of the disclosure, the request may includequality information about the image data requested by the terminal 1220.For example, the request may include information about at least one of abitrate or a resolution. As another example, the request may includequality information and information indicating whether AI downscalinghas been applied. As another example, a request message may includecapability information of the terminal 1220 and quality informationabout the image data requested by the terminal 1220.

FIG. 15B is a flowchart for describing a method of streaming data, themethod being performed by the terminal 1220, according to embodiments ofthe disclosure.

In operation S1515, the terminal 1220 may receive, from a server 1210,additional information of a plurality of items of image data ofdifferent qualities. According to embodiments of the disclosure, theterminal 1220 may request the server 1210 for the additional informationof the plurality of items of image data. However, this is an example,and the server 1210 may transmit the additional information to theterminal 1220 when a communication session for streaming image data isestablished between the terminal 1220 and the server 1210.

In the present embodiment of the disclosure, the additional informationmay correspond to that described with reference to FIG. 13B.

In operation S1525, the terminal 1220 may request the server 1210 forimage data from among the plurality of items of image data, based on theadditional information. According to embodiments of the disclosure, theterminal 1220 may request the server 1210 for image data whose qualityis set by a user. For example, when the user of the terminal 1220selects a quality of FHD and 5 Mbps, the terminal 1220 may request theserver 1210 for image data of FHD and 5 Mbps from the terminal 1220.However, this is an example, and according to embodiments of thedisclosure, the terminal 1220 may request the server 1210 for image dataof a quality set as a default. For example, in a case in which streamingof image data starts between the terminal 1220 and the server 1210, whena state of a network between the terminal 1220 and the server 1210 isnot confirmed, the terminal 1220 may request the server 1210 for imagedata of a lowest quality from among a plurality of qualities. Accordingto embodiments of the disclosure, the terminal 1220 may request theserver 1210 for image data of a particular quality (e.g., HD and 4 Mbps)set as a default.

According to embodiments of the disclosure, the terminal 1220 mayrequest the server 1210 for image data by specifying information aboutwhether AI encoding has been performed, in addition to a quality.

In operation S1535, when the terminal 1220 receives image data and AIdata which correspond to the request, the terminal 1220 may determinewhether to perform AI upscaling on the received image data, based on theAI data.

According to embodiments of the disclosure, the terminal 1220 maydetermine, based on the AI data, whether the received image data hasbeen AI encoded through the first DNN trained jointly with the secondDNN. The AI data may include information about at least one ofinformation indicating whether AI downscaling has been applied, an AIscale conversion level, or DNN configuration information used in AIupscaling.

According to embodiments of the disclosure, when the terminal 1220includes the information that specifies AI-encoded image data in arequest message for image data in operation S1525, the terminal 1220 maydetermine that AI downscaling has been applied to image data received inresponse to the request message.

In operation S1545, the terminal 1220 may perform, based on a result ofthe determination about whether to perform AI upscaling, AI upscaling onthe received image data through the DNN for upscaling trained jointlywith the DNN for downscaling of the server 1210.

When the terminal 1220 determines that the received image data is imagedata that has been AI encoded through the first DNN trained jointly withthe second DNN, the terminal 1220 may perform, through the second DNN,AI upscaling on the received image data. In the present embodiment ofthe disclosure, a method, performed by the terminal 1220, of performingAI upscaling on received image data through the second DNN maycorrespond to operation S1335 described above with reference to FIG.13B.

In operation S1555, the terminal 1220 may confirm the state of thenetwork between the terminal 1220 and the server 1210. The state of thenetwork may be determined based on a timestamp and a BER of image datareceived by the terminal 1220. For example, as a result of determinationbased on the timestamp, when the terminal 1220 determines that a time ofreceiving the image data from the server 1210 is delayed, the terminal1220 may determine that the state of the network is congested. Asanother example, when the BER is less than a predetermined reference,the terminal 1220 may determine that the state of the network is notcongested. However, this is an example, and the state of the network maybe determined based on other information.

In operation S1565, according to the state of the network, the terminal1220 may request the server 1210 for image data of a different qualityfrom among the plurality of items of image data, based on the additionalinformation.

For example, although the terminal 1220 requested AI-encoded image dataof FHD and 5 Mbps in aforementioned operation S1525, when an interval oftimestamps of image data received thereafter becomes short or a BER isdecreased, the terminal 1220 may determine that a congestion level ofthe network is improved and thus may request the server 1210 forAI-encoded image data of 4K and a 10-Mbps bitrate.

As another example, although the terminal 1220 requested AI-encodedimage data of 4K and 10 Mbps in aforementioned operation S1525, when aninterval of timestamps of image data received thereafter becomes long ora BER is increased, the terminal 1220 may determine that the congestionlevel of the network deteriorates and thus may request the server 1210for AI-encoded image data of FHD and 5 Mbps.

As another example, although the terminal 1220 requested AI-encodedimage data of FHD and 5 Mbps in aforementioned operation S1515, when aninterval of timestamps of image data received thereafter becomes long ora BER is increased, the terminal 1220 may determine that the congestionlevel of the network deteriorates and thus may request the server 1210for image data of HD and 1 Mbps. That is, when a resolution and abitrate are less than a predetermined reference, the terminal 1220 mayconsider a level of image data that is to be reconstructed by AIupscaling, and thus may request the server 1210 for image data on whichAI downscaling has not been performed. However, this is an example, anda method, performed by the terminal 1220, of changing a quality of imagedata based on a change in a state of a network is not limited to theaforementioned example.

Information included in a request message transmitted from the terminal1220 to the server 1210 so as to request the image data of the differentquality corresponding to the state of the network may correspond to theaforementioned descriptions provided with reference to operation S1515.

FIG. 16 is a diagram for describing a method of performing streamingbetween a server 1610 and a first terminal 1620 according to whether thefirst terminal 1620 supports AI upscaling, according to embodiments ofthe disclosure.

In the embodiment of FIG. 16, it is assumed that the first terminal 1620corresponds to a terminal that can support AI upscaling through a secondDNN trained jointly with a first DNN of the server 1610, and a secondterminal 1630 corresponds to a terminal that does not support AIupscaling.

The server 1610 stores a plurality of items of image data of differentqualities for adaptive streaming, and transmits image data, in responseto a request from a terminal (e.g., the first terminal 1620). Theterminal (e.g., the first terminal 1620) may obtain additionalinformation of the plurality of items of image data from the server1610, and may request the server 1610 for one of the plurality of itemsof image data, based on the additional information. A plurality ofpieces of information included in the additional information will befurther described below with reference to FIGS. 20 to 23.

For example, the server 1610 may obtain image data of 4K and 20 Mbps1642 that is AI downscaled by performing AI downscaling on originalimage data of 8K and 60 Mbps 1640 through a 1a-1 DNN 1612. The server1610 may transmit the AI-encoded image data of 4K and 20 Mbps 1642together with AI data related to AI downscaling to 4K and 20 Mbps, inresponse to a request from the first terminal 1620. The first terminal1620 may perform AI upscaling on the received image data through a 2a-1DNN 1622 trained jointly with the 1a-1 DNN 1612, and thus may obtain AIupscaled image data 1652. In this regard, the first terminal 1620 mayperform aforementioned AI upscaling by obtaining at least one ofinformation indicating whether AI downscaling has been applied, an AIscale conversion level, or DNN configuration information used in the AIupscaling, based on information included in the received AI data.

As another example, the server 1610 may obtain image data of FHD and 7Mbps 1644 that is AI downscaled by performing AI downscaling on theoriginal image data of 8K and 60 Mbps 1640 through a 1a-2 DNN 1614. Theserver 1610 may transmit the AI-encoded image data of FHD and 7 Mbps1644 together with AI data related to AI downscaling to FHD and 7 Mbps,in response to a request from the first terminal 1620. For example,while the first terminal 1620 requests and receives the AI-encoded imagedata of 4K and 20 Mbps 1642 as in the aforementioned example, when thefirst terminal 1620 determines that a congestion level of a networkdeteriorates, the first terminal 1620 may request the AI-encoded imagedata of FHD and 7 Mbps 1644. The first terminal 1620 may perform AIupscaling on the image data, which is received in response to therequest, through a 2a-2 DNN 1624 trained jointly with the 1a-2 DNN 1614,and thus may obtain AI upscaled image data 1654. As in theaforementioned example, the first terminal 1620 may use information inAI upscaling, the information being included in the AI data.

As another example, the server 1610 may obtain image data of HD and 4Mbps 1646 that is AI downscaled by performing AI downscaling on theoriginal image data of 8K and 60 Mbps 1640 through a 1a-3 DNN 1616. Theserver 1610 may transmit the AI-encoded image data of HD and 4 Mbps 1646together with AI data related to AI downscaling to HD and 4 Mbps, inresponse to a request from the first terminal 1620. The first terminal1620 may perform AI upscaling on the received image data through a 2a-3DNN 1626 trained jointly with the 1a-3 DNN 1616, and thus may obtain AIupscaled image data 1656. As in the aforementioned example, the firstterminal 1620 may use information in AI upscaling, the information beingincluded in the AI data.

Also, the first terminal 1620 may additionally perform legacy upscalingon AI upscaled image data. For example, due to a state of a network, thefirst terminal 1620 may receive the AI-encoded image data of HD and 4Mbps 1646 obtained by applying AI downscaling through the 1a-3 DNN 1616.The first terminal 1620 may obtain the AI upscaled image data 1656 ofFHD and 7 Mbps by performing AI upscaling through the 2a-3 DNN 1626trained jointly with the 1a-3 DNN 1616. The first terminal 1620 mayperform legacy upscaling on the AI upscaled image data 1656 of FHD and 7Mbps and thus may obtain image data of 4K and 20 Mbps.

In the aforementioned example, it is described that the first terminal1620 receives AI-encoded image data through a first DNN, but the firstterminal 1620 may receive image data that is not AI downscaled (e.g.,image data 1650).

The second terminal 1630 is a terminal that does not support upscalingthrough a second DNN trained jointly with the first DNN. The secondterminal 1630 may not determine whether image data 1660, 1662, 1664, or1666 received from the server 1610 is image data on which AI downscalinghas been performed through the first DNN, and may process the receivedimage data 1660, 1662, 1664, or 1666. In a case of image data on whichdownscaling has been performed through the first DNN, the image data mayhave less quality loss compared to image data on which a generaldownscale technique has been performed, and thus, even when the secondterminal 1630 does not support upscaling through the second DNN, thesecond terminal 1630 may be provided image data of a high quality,compared to image data based on the related art.

FIG. 17 is a diagram for describing a method of performing streamingbetween a server 1710 and a first terminal 1720 according to whether thefirst terminal 1720 supports AI upscaling, according to embodiments ofthe disclosure.

In the embodiment of FIG. 17, it is assumed that the first terminal 1720corresponds to a terminal that can support AI upscaling through a secondDNN trained jointly with a first DNN of the server 1710, and a secondterminal 1730 corresponds to a terminal that does not support AIupscaling.

The server 1710 stores a plurality of items of image data of differentqualities for adaptive streaming, and transmits image data, in responseto a request from a terminal (e.g., the first terminal 1720). Theterminal (e.g., the first terminal 1720) may obtain additionalinformation of the plurality of items of image data from the server1710, and may request the server 1710 for one of the plurality of itemsof image data, based on the additional information. A plurality ofpieces of information included in the additional information will befurther described below with reference to FIGS. 21 to 24.

For example, the server 1710 may obtain image data of 4K and 20 Mbps1742 that is AI downscaled by performing AI downscaling on originalimage data of 8K and 60 Mbps 1740 through a 1b-1 DNN 1712. The server1710 may transmit the AI-encoded image data of 4K and 20 Mbps 1742together with AI data related to AI downscaling to 4K and 20 Mbps, inresponse to a request from the first terminal 1720. The first terminal1720 may perform AI upscaling on the received image data through a 2b-1DNN 1722 trained jointly with the 1b-1 DNN 1712, and thus may obtain AIupscaled image data 1752. In this regard, the first terminal 1720 mayperform aforementioned AI upscaling by obtaining at least one ofinformation indicating whether AI downscaling has been applied, an AIscale conversion level, or DNN configuration information used in the AIupscaling, based on information included in the received AI data.

As another example, the server 1710 may perform AI downscaling on the AIdownscaled image data of 4K 1742 through a 1b-2 DNN 1714, and thus mayobtain AI downscaled image data of FHD and 7 Mbps 1744. The server 1710may transmit the AI downscaled image data of FHD and 7 Mbps 1744together with AI data related to AI downscaling to FHD and 7 Mbps, inresponse to a request from the first terminal 1720. For example, whilethe first terminal 1720 requests and receives the AI-encoded image dataof 4K and 20 Mbps 1742 as in the aforementioned example, when the firstterminal 1720 determines that a congestion level of a networkdeteriorates, the first terminal 1720 may request the AI-encoded imagedata of FHD and 7 Mbps 1744. The first terminal 1720 may perform AIupscaling on the image data, which is received in response to therequest, through a 2b-2 DNN 1724 trained jointly with the 1b-2 DNN 1714,and thus may obtain AI upscaled image data 1754. As in theaforementioned example, the first terminal 1720 may use information inAI upscaling, the information being included in the AI data.

As another example, the server 1710 may perform AI downscaling on theAI-encoded image data of FHD and 7 Mbps 1744 through a 2b-3 DNN 1716,and thus may obtain AI downscaled image data of HD and 4 Mbps 1746. Theserver 1710 may transmit the AI downscaled image data of HD and 4 Mbps1746 together with AI data related to AI downscaling to HD and 4 Mbps,in response to a request from the first terminal 1720. The firstterminal 1720 may perform AI upscaling on the received image datathrough a 2b-3 DNN 1726 trained jointly with the 1b-3 DNN 1716, and thusmay obtain AI upscaled image data 1756. As in the aforementionedexample, the first terminal 1720 may use information in AI upscaling,the information being included in the AI data.

In FIG. 17, the server 1710 is illustrated as obtaining the AIdownscaled image data of FHD and 7 Mbps 1744 through two separatedownscaling processes using the 1b-1 DNN 1712 and the 1b-2 DNN 1714, butthe embodiments are not limited thereto, and the server 1710 maydownscale the original image data of 8K and 60 Mbps 1740 directly to theimage data of FHD 1744 using a single DNN, which is jointly trained witha corresponding DNN of the first terminal 1720. Also, the server 1710may downscale the original image data of 8K and 60 Mbps 1740 directly tothe image data of HD 1746 using a single DNN, which is jointly trainedwith a corresponding DNN of the first terminal 1720.

In the aforementioned example, it is described that the first terminal1720 receives AI-encoded image data through a first DNN, but the firstterminal 1720 may receive image data that is not AI downscaled (e.g.,image data 1750).

A structure of a DNN shown in FIG. 17 is an example, and at least one ofthe 1b-1 DNN 1712, the 1b-2 DNN 1714, or the 1b-3 DNN 1716 may bereplaced with a legacy scaler. Also, in association thereto, at leastone of the 2b-1 DNN 1722, the 2b-2 DNN 1724, or the 2b-3 DNN 1726 may bereplaced with a legacy scaler.

The second terminal 1730 is a terminal that does not support upscalingthrough a second DNN trained jointly with the first DNN. The secondterminal 1730 may not determine whether image data 1760, 1762, 1764, or1766 received from the server 1710 is image data on which AI downscalinghas been performed through the first DNN, and may process the receivedimage data 1760, 1762, 1764, or 1766. In a case of image data on whichdownscaling has been performed through the first DNN, the image data mayhave less quality loss compared to image data on which a generaldownscale technique has been performed, and thus, even when the secondterminal 1730 does not support upscaling through the second DNN, thesecond terminal 1730 may be provided image data of a high quality,compared to image data based on the related art.

The image data shown in FIGS. 12 and 17 is an example, and a pluralityof items of image data stored in different qualities are not limited toAI-encoded 4K image data, AI-encoded FHD image data, AI-encoded HD imagedata, or the like. For example, the server 1710 may perform AIdownscaling on 8K original image data and thus may store a plurality ofitems of AI-encoded image data including 5K (5120×2880) image data, 3K(2560×1440) image data, 540p (960×540) image data, 360p (640×360) imagedata, or the like. Also, the server 1710 may store a plurality of itemsof image data of different bitrates that are with respect to image dataof a particular resolution. For example, AI-encoded 4K image data may bestored as image data of 4K and 20 Mbps, image data of 4K and 15 Mbps, orthe like. To this end, the first DNN structure shown in FIGS. 16 and 17may be variously configured. That is, a first DNN structure forconverting 8K image data to AI-encoded 3K image data, a first DNNstructure for converting 3K image data to AI-encoded 540p image data, afirst DNN structure for converting image data of 8K and 60 Mbps toAI-encoded image data of 4K and 15 Mbps, or the like may be used.

FIG. 18 is a diagram for describing a method, performed by the server1210, of streaming image data according to a capability of the terminal1220, according to embodiments of the disclosure.

In operation S1810, the terminal 1220 may transmit information about thecapability to the server 1210. According to embodiments of thedisclosure, the information about the capability may include at leastone of information indicating whether the terminal 1220 can change aquality of image data requested adaptive to a state of a network,information indicating whether the terminal 1220 can support AIupscaling through a second DNN, or information about an AI upscale levelsupportable by the terminal 1220. However, this is an example, and theinformation about the capability may include information about codecsupported by the terminal 1220.

In operation S1820, the terminal 1220 may request the server 1210 foradditional information. For adaptive streaming between the server 1210and the terminal 1220, it is required to check respective qualities of aplurality of items of image data providable from the server 1210 andwhether AI encoding has been performed thereto. Accordingly, theterminal 1220 may request the server 1210 for the additionalinformation. The additional information will be further described belowwith reference to FIGS. 21 to 24. Accordingly, the terminal 1220 mayrequest the server 1210 for additional information of the plurality ofitems of image data.

In operation S1830, the server 1210 may transmit the additionalinformation to the terminal 1220. When the server 1210 receives therequest from the terminal 1220, the server 1210 may determine theadditional information corresponding to the request. For example, theserver 1210 may determine the additional information that is from amonga plurality of pieces of additional information stored in the server1210 and is requested by the terminal 1220, based on an identifier ofthe additional information included in the request from the terminal1220. The additional information may be directly generated by the server1210, but according to embodiments of the disclosure, the additionalinformation may be received from a different server.

In operation S1840, the terminal 1220 may request the server 1210 forimage data of a quality corresponding to a state of a network, based onthe additional information.

In the present embodiment of the disclosure, it is assumed that arequest message transmitted from the terminal 1220 to the server 1210 soas to request image data includes only quality information correspondingto the state of the network. For example, the terminal 1220 maytransmit, to the server 1210, a request message including informationindicating that a resolution of the image data corresponding to thestate of the network corresponds to FHD, and a bitrate of the image datacorresponds to 5 Mbps.

In operation S1850, the server 1210 may determine image datacorresponding to a request, based on the capability of the terminal1220.

When image data of a particular quality is requested, the server 1210may determine whether to transmit image data on which downscaling hasbeen performed. As a result of determination by the server 1210 based onthe capability of the terminal 1220, when the terminal 1220 can supportAI upscaling through a second DNN trained jointly with a first DNN, theserver 1210 may transmit AI-encoded image data.

Also, the server 1210 may determine which image data is to betransmitted, based on an AI upscale level supportable by the terminal1220, the image data being AI downscaled to a certain level. Forexample, in a case in which the terminal 1220 requested AI-encoded imagedata of FHD and 5 Mbps, the server 1210 may determine whether totransmit image data of FHD and 5 Mbps obtained by performing AIdownscaling on image data of 8K and 30 Mbps through a 1a DNN, or imagedata of FHD and 5 Mbps obtained by performing AI downscaling on imagedata of 4K and 10 Mbps through a 1b DNN. The 1a DNN and the 1b DNN mayhave different DNN configuration information.

Table 1 below includes values of a resolution and a bitrate which areprovidable from the server 1210 to the terminal 1220. When describing ascale conversion level with reference to Table 1, a difference between4K & 20 Mbps and 4K and 10 Mbps, and a difference between 4K & 10 Mbpsand FHD and 5 Mbps may each be defined as one level. However, this is anexample, and resolutions and bitrates that are supported by a streamingsystem according to the disclosure are not limited to the values inTable 1.

TABLE 1 Resolution Bitrate 8K 40 Mbps 30 Mbps 4K 20 Mbps 10 Mbps FHD  5Mbps HD  1 Mbps

In operation S1860, the server 1210 may transmit AI data and image databased on the determination to the terminal 1220. The AI data may includeinformation required for the terminal 1220 to AI upscale AI-encodedimage data, and may correspond to the descriptions provided withreference to FIG. 5.

In operation S1870, the terminal 1220 may perform AI upscaling on thereceived image data through the second DNN trained jointly with thefirst DNN.

The terminal 1220 may determine, based on the AI data, whether to applyAI upscaling to the received image data through the second DNN trainedjointly with the first DNN. When the AI data includes informationindicating that the received image data is AI-encoded image data, theterminal 1220 may perform AI upscaling through the second DNN on thereceived image data. Also, the AI data may include information about atleast one of an AI scale conversion level or DNN configurationinformation used in AI upscaling. For example, the AI data may includeinformation indicating whether the AI-encoded image data of FHD and 5Mbps is generated by AI downscaling the image data of 8K and 30 Mbpsthrough the 1a DNN or by AI downscaling the image data of 4K and 10 Mbpsthrough the 1b DNN.

FIG. 19 is a diagram for describing a method, performed by the server1210, of streaming image data according to a state of a network and acapability of the terminal 1220, according to embodiments of thedisclosure.

In operation S1910, the terminal 1220 may transmit information about thecapability to the server 1210. According to embodiments of thedisclosure, the information about the capability may include at leastone of information indicating whether the terminal 1220 can change aquality of image data requested adaptive to a state of a network,information indicating whether the terminal 1220 can support AIupscaling through a second DNN, or information about an AI upscale levelsupportable by the terminal 1220. However, this is an example, and theinformation about the capability may include information about codecsupported by the terminal 1220.

In operation S1920, the terminal 1220 may request the server 1210 foradditional information. The terminal 1220 may request the server 1210for additional information of image data. Operation S1920 may correspondto operation S1820 described above with reference to FIG. 18.

In operation S1930, the server 1210 may transmit the additionalinformation to the terminal 1220. The server 1210 may transmit theadditional information, in response to the request from the terminal1220. Operation S1930 may correspond to operation S1830 described abovewith reference to FIG. 18.

In operation S1940, the terminal 1220 may request the server 1210 forimage data of a particular quality, based on the additional information.For example, the terminal 1220 may request the server 1210 for imagedata of 8K and 30 Mbps, according to selection by a user. Also, theterminal 1220 may include information about a state of a network in therequest for the image data of a particular quality, and may transmit therequest. For example, the terminal 1220 may include, in a requestmessage, information about a BER, a timestamp, or the like ofpreviously-received image data, and may transmit the request message tothe server 1210.

In operation S1950, the server 1210 may determine image datacorresponding to the request, based on the state of the network and thecapability of the terminal 1220.

The server 1210 may determine a quality of the image data correspondingto the state of the network, based on the information about the state ofthe network included in the request received from the terminal 1220. Forexample, the server 1210 may determine the quality of the image data tobe 4K and 20 Mbps, the quality of the image data corresponding to thestate of the network.

As described above in operation S1940, when it is determined that theterminal 1220 requested the image data of 8K and 30 Mbps but the requestfrom the terminal 1220 does not correspond to the state of the network,the server 1210 may determine whether the terminal 1220 supports AIupscaling through the second DNN trained jointly with the first DNN,based on the information about the capability of the terminal 1220. As aresult of the determination by the server 1210, when the terminal 1220supports AI upscaling, the server 1210 may determine to transmit, to theterminal 1220, image data of 4K and 20 Mbps that is generated byperforming AI downscaling on the image data of 8K and 30 Mbps throughthe first DNN.

In operation S1960, the server 1210 may transmit AI data and the imagedata to the terminal 1220, based on the determination. The AI data mayinclude information required for the terminal 1220 to AI upscaleAI-encoded image data, and may correspond to the descriptions providedwith reference to FIG. 12.

In operation S1970, the terminal 1220 may perform AI upscaling on thereceived image data through the second DNN trained jointly with thefirst DNN.

The terminal 1220 may determine, based on the AI data, whether to applyAI upscaling to the received image data through the second DNN trainedjointly with the first DNN. When the AI data includes informationindicating that the received image data is AI-encoded image data, theterminal 1220 may perform AI upscaling through the second DNN on thereceived image data. Also, the AI data may include information about atleast one of an AI scale conversion level or DNN configurationinformation used in AI upscaling. For example, the AI data may includeinformation indicating that the AI-encoded image data of 4K and 20 Mbpsis generated by AI encoding the image data of 8K and 30 Mbps. As anotherexample, the AI data may include an AI scale conversion level, and theterminal 1220 may determine DNN configuration information, based on theresolution and the bitrate.

FIG. 20 is a diagram for describing a method, performed by the terminal1220, of streaming image data corresponding to a state of a network,based on additional information and a capability, according toembodiments of the disclosure.

In operation S2010, the terminal 1220 may transmit information about thecapability to the server 1210. According to embodiments of thedisclosure, the information about the capability may include at leastone of information indicating whether the terminal 1220 can change aquality of image data requested adaptive to the state of the network,information indicating whether the terminal 1220 can support AIupscaling through a second DNN, or information about an AI upscale levelsupportable by the terminal 1220. However, this is an example, and theinformation about the capability may include information about codecsupported by the terminal 1220.

In operation S2020, the terminal 1220 may request the server 1210 forthe additional information. The terminal 1220 may request the server1210 for the additional information of image data. Operation S2020 maycorrespond to S1820 described above with reference to FIG. 18.

In operation S2030, the server 1210 may transmit the additionalinformation to the terminal 1220. The server 1210 may transmit theadditional information to the terminal 1220, in response to the requestfrom the terminal 1220. Operation S2030 may correspond to S1830described above with reference to FIG. 18.

In operation S2040, the terminal 1220 may identify the image data thatcorresponds to the state of the network and is from among a plurality ofitems of image data, based on the additional information and thecapability of the terminal 1220.

For example, the terminal 1220 may check, based on the additionalinformation, respective qualities and whether AI encoding has beenperformed about the plurality of items of image data that can beprovided by the server 1210, and various types of DNN configurationinformation which can be used in performing AI upscaling on image dataof a particular quality. The additional information will be described indetail with reference to FIGS. 21 to 24.

The terminal 1220 may determine, based on the capability of the terminal1220, the image data that corresponds to the state of the network and isfrom among the plurality of items of image data checked based on theadditional information. For example, when a quality of the image datathat corresponds to the state of the network is FHD and 5 Mbps, theterminal 1220 may determine, based on the capability of the terminal1220, one of AI-encoded image data of FHD and 5 Mbps generated byperforming AI downscaling on image data of 8K and 30 Mbps through the 1aDNN and AI-encoded image data of FHD and 5 Mbps generated by performingAI downscaling on image data of 4K and 10 Mbps through the 1b DNN,wherein the image data of 8K and 30 Mbps and the image data of 4K and 10Mbps are providable from the server 1210. When the terminal 1220supports AI upscaling through a 2b DNN trained jointly with a 1b DNN,the terminal 1220 may determine, from among the plurality of items ofimage data, the AI-encoded image data of FHD and 5 Mbps generated byperforming AI downscaling on the image data of 4K and 10 Mbps throughthe 1b DNN.

In operation S2050, the terminal 1220 may request the server 1210 forthe determined image data. A request message transmitted from theterminal 1220 to the server 1210 so as to request the determined imagedata may include an identifier of the determined image data. Forexample, the request message may include an identifier of the AI-encodedimage data of FHD and 5 Mbps generated by performing AI downscaling onthe image data of 4K and 10 Mbps through the 1b DNN.

The terminal 1220 may request the server 1210 for the determined imagedata in a unit of a segment. The segment may be generated bypartitioning the image data, based on a time unit. When the terminal1220 requests the determined image data in a unit of a segment, therequest message may include not only information about a quality of thedetermined image data, whether AI encoding has been performed thereon,or the like but may also include an identifier of the segment. Theidentifier of the segment may include a segment number, an offset, orthe like, but this is an example and thus the identifier of the segmentis not limited to the aforementioned examples. The segment number refersto each of numbers respectively allocated to a plurality of segmentsincluded in the image data. Also, the offset refers to a differencebetween a preset reference time and a start time of the segment. Here,the preset reference time may be a start time of a first segment or aninitialization segment from among the plurality of segments included inthe image data.

In operation S2060, the server 1210 may transmit AI data and the imagedata corresponding to a request. The AI data may include informationrequired for the terminal 1220 to AI upscale the AI-encoded image data,and may correspond to the descriptions provided with reference to FIG.5.

In operation S2070, the terminal 1220 may perform AI upscaling on thereceived image data through the second DNN trained jointly with thefirst DNN.

The terminal 1220 may perform AI upscaling on the received image data,based on information about at least one of an AI scale conversion levelor DNN configuration information used in AI upscaling, which is includedin the AI data. A method by which the terminal 1220 performs AIupscaling on the received image data may correspond to the descriptionsprovided with reference to FIG. 2.

FIG. 21 is a diagram for describing additional information provided forstreaming, according to embodiments of the disclosure.

Referring to FIG. 21, the additional information may hierarchicallyinclude an image data set element 2110, an image data element 2120, anda segment element 2130. Each of the aforementioned elements 2110, 2120,and 2130 may include a plurality of pieces of information indicating animage data set, image data, and an attribute of a segment. The imagedata set may be a group of a plurality of items of interchangeable imagedata. For example, the image data set may be a group of a plurality ofitems of image data generated by encoding a first period of content atdifferent qualities, and may correspond to an adaptation set in theMPEG-DASH. The segment may be a portion generated by partitioning theimage data, based on a time.

The image data set element 2110 may include information about a type2112 and identification (ID) 2114 of each of image data sets. In thisregard, the type 2112 may indicate a type of content included in theimage data set, and examples of the type may include an image, audio, atext, or the like. The ID 2114 may include identifiers for identifyingthe image data sets, respectively.

The image data element 2120 may include ID 2122, a quality 2124, AIscale conversion information 2126, or the like of image data. The ID2122 may include an identifier for identifying the image data, and thequality 2124 may include various attributes including a bitrate, aresolution, or the like. The AI scale conversion information 2126 mayfurther include information about codec appropriate for a second DNNused in AI upscaling conversion, information about AI upscaling levelsthat are available in respective conversions of the plurality of itemsof image data, information about a parameter set of the second DNNcorresponding to a parameter set used in a first DNN, or the like.

The segment element 2130 may include information about ID 2132, anoffset 2134, or the like of the segment. The ID 2132 may include anidentifier for identifying the segment, and the offset 2134 may includeinformation about a position of the segment on a timeline. In a case inwhich a quality of image data to be received has to be changed due to achange in a state of a network, the offset 2134 may be used tosynchronize image data of a previous quality with image data of aquality to be newly received. For example, when the plurality of itemsof image data consist of segments whose time offsets are 2 ms, 4 ms, 6ms, and 8 ms, respectively, a terminal may receive segments up to 4 mswith respect to image data of 4K and 10 Mbps, and afterward, when theterminal requests image data of FHD and 5 Mbps due to a change in thestate of the network, the terminal may process a segment of 6 ms to bereproduced according to synchronization with the image data of 4K and 10Mbps.

A structure of the additional information shown in FIG. 21 is anexample, and additional information for adaptive streaming is notlimited thereto. As another example, the AI scale conversion information2126 may be included in the segment element 2130. As another example,the additional information may additionally include parameter updateinformation by which the terminal can update the parameter of the secondDNN jointly with the parameter of the first DNN of a server. However,this is an example, and information for updating the parameter of thesecond DNN may be provided to the terminal, separately from theadditional information.

FIG. 22 is a diagram for describing detail configuration of additionalinformation, according to embodiments of the disclosure.

Referring to FIG. 22, a “mediadataset” attribute may be defined in theadditional information according to embodiments of the disclosure. The“mediadataset” attribute is to indicate an attribute of a media data setconsisting of a plurality of items of media data of different qualities,and may include an “id” element indicating an identifier of the mediadata set, a “type” element indicating a type of content, or the like.The present embodiment of the disclosure corresponds to a case in whichmedia data is image data, and the “type” element may be set as a video.Hereinafter, descriptions will be provided assuming that the media datais the image data.

The “mediadataset” attribute defines an attribute of each of a pluralityof items of image data of different qualities, and may include an “id”element indicating an identifier of the media data, a “resolution”element indicating a resolution, a “bitrate” element indicating abitrate, an “AIupscale” element indicating whether it is required toapply AI upscaling, or the like. A terminal may check whether image datahas been AI downscaled through a first DNN trained jointly with a secondDNN of the terminal, based on the “AIupscale” element of each imagedata. The “AIupscale” element may be included in the aforementioned AIscale conversion information.

The terminal may check an attribute of each image data included inadditional information, and may request particular image data, based onthe attribute. For example, the terminal may request a server forAI-encoded image data of 4K and 10 Mbps from among the plurality ofitems of image data. In this case, the terminal may transmit, to theserver, a request message including information about id=2.

When the terminal receives image data corresponding to the request, theterminal may perform AI upscaling on the received image data through thesecond DNN trained jointly with the first DNN of the server. In thisregard, the terminal may obtain DNN configuration information that isoptimized for the second DNN to perform AI upscaling on the image data,based on information about a resolution, a bitrate, or the like of theimage data. The DNN configuration information may include informationabout filter kernels (e.g., the number of convolution layers, the numberof filter kernels according to each convolution layer, a parameter ofeach filter kernel, or the like). For example, the terminal may includeinformation that has been trained jointly with the first DNN of theserver so as to indicate that upscaling through the second DNN has to beperformed on image data of 4K and 10 Mbps by using A DNN configurationinformation.

According to embodiments of the disclosure, the DNN configurationinformation that is optimized for the second DNN to perform AI upscalingmay vary according to not only a resolution and a bitrate of image databut also according to a genre of content consisting of the plurality ofitems of image data. For example, the terminal may include informationthat has been trained jointly with the first DNN of the server so as toindicate that, for a sports genre, upscaling through the second DNN hasto be performed on AI-encoded image data of 4K and 10 Mbps by using DNNconfiguration information corresponding to the sports genre and 4K & 10Mbps, and for a drama genre, upscaling through the second DNN has to beperformed on AI-encoded image data of 4K and 10 Mbps by using DNNconfiguration information corresponding to the drama genre and 4K & 10Mbps.

FIG. 23 is a diagram for describing detail configuration of additionalinformation, according to embodiments of the disclosure.

Referring to FIG. 23, a “mediadataset” attribute and a “mediadata”attribute may be defined in the additional information according toembodiments of the disclosure. In the present embodiment of thedisclosure, descriptions corresponding to embodiments of the disclosurewhich is described above with reference to FIG. 22 are not provided, andan “AIupscalelevel” element different therefrom will now be described indetail.

The “AIupscalelevel” element included in the “mediadata” attributeindicates a difference between AI-encoded image data and original imagedata.

As described above with reference to FIG. 5, because the AI encodingprocess according to embodiments of the disclosure is performed based onboth a resolution and a bitrate, information of the difference betweenthe AI-encoded image data and the original image data may be provided.In the embodiment of FIG. 23, image data of 8K and 40 Mbps correspondsto the original image data. In the present embodiment of the disclosure,a value of the “AIupscalelevel” element may be determined based on adifference between a bitrate and a resolution of the image data of 8Kand 40 Mbps (id=n) and a bitrate and a resolution of the AI-encodedimage data.

For example, AI-encoded image data whose id is 1 may have been generatedby performing AI encoding on the original image data, based on a bitrateof 30 Mbps, and AI-encoded image data whose id is 2 may have beengenerated by performing AI encoding on the original image data, based ona resolution of 4K and a bitrate of 20 Mbps. The value of the“AIupscalelevel” element according to embodiments of the disclosure isan example of the difference in resolutions and bitrates of the originalimage data and the AI-encoded image data and thus is not limited to theexample.

The terminal may select, based on a capability of the terminal, imagedata on which the terminal can perform AI upscaling from among two levelAI-encoded image data of 4K and 20 Mbps, and three-level AI-encodedimage data of 4K and 10 Mbps, and may request the selected image data.

In the embodiment of FIG. 23, image data whose id is n+1 and image datawhose id is n+2 correspond to image data obtained by performingdownscaling using a legacy downscaler.

FIG. 24 is a diagram for describing detail configuration of additionalinformation, according to embodiments of the disclosure.

Referring to FIG. 24, a “mediadataset” attribute and a “mediadata”attribute may be defined in the additional information according toembodiments of the disclosure. In the present embodiment of thedisclosure, descriptions corresponding to embodiments of the disclosurewhich is described above with reference to FIG. 22 are not provided, andan “AIupscaleparameterset” element different therefrom will now bedescribed in detail.

The “AIupscaleparameterset” element included in the “mediadata”attribute may provide information about a plurality of pieces of variousDNN configuration information that are usable in performing AI upscalingon AI downscaled image data. For example, even for a plurality of itemsof AI-encoded image data of a same quality of 4K and 20 Mbps, the numberof DNN convolution layers and a size and number of filter kernels, whichare used in AI upscaling the for AI-encoded image data, may vary andthus various DNN configuration information may exist.

When a plurality of items of AI-encoded image data of a same qualityhave different DNN configuration information, the terminal may determineone of the plurality of items of AI-encoded image data of the samequality, based on the capability of the terminal. For example, when anAI up-scaler of the terminal includes B AIupscaleparameterset, theterminal may select, from among the plurality of items of AI-encodedimage data of the same quality, AI-encoded image data that can bereconstructed in a corresponding DNN. As another example, when the AIup-scaler of the terminal includes A=AIupscaleparameterset having acomplicated configuration compared to that of the aforementionedexample, the terminal may reconstruct all of the plurality of items ofAI-encoded image data of the same quality. In this case, the terminalmay select, from among the plurality of items of AI-encoded image dataof the same quality, AI-encoded image data that uses a relatively lessnetwork resource in streaming or that can be reconstructed to a higherquality by the terminal, according to configuration.

The terminal may obtain at least one piece of DNN configurationinformation from among a plurality of pieces of DNN configurationinformation, based on a hardware specification of the terminal or codec.For example, the terminal may obtain DNN configuration information thatcorresponds to the terminal and is from among a plurality of pieces ofDNN configuration information that are applicable to AI upscaling withrespect to same AI-encoded image data of 4K and 20 Mbps. Accordingly,the terminal may request a server for image data that has been AIencoded based on the DNN configuration information corresponding to 4Kand 20 Mbps.

FIG. 25 is a diagram for describing AI data 2510 and image data 2520that are streamed from a server to a terminal, according to embodimentsof the disclosure.

Referring to FIG. 25, in response to a request from the terminal, theserver may transmit, to the terminal, the AI data 2510 and the imagedata 2520 that correspond to the request.

The AI data 2510 may include information indicating whether AIdownscaling has been performed on the image data 2520. Also, when theimage data 2520 has been AI encoded, the AI data 2510 may includeinformation about at least one of an AI scale conversion level or DNNconfiguration information used in AI upscaling. According to embodimentsof the disclosure, the AI data 2510 may include the AI scale conversionlevel, and the DNN configuration information for AI upscaling may bedetermined by the terminal based on a resolution and a bitrate.

The AI data 2510 may correspond to an initialization segment of theMPEG-DASH, and the terminal may determine whether the image data 2520has been AI upscaled through a second DNN trained jointly with a firstDNN of the server.

The AI data 2510 may include other information required for the terminalto decode the image data 2520, in addition to the aforementionedinformation. For example, the AI data 2510 may include information abouta type of codec, ID, an offset, or the like.

The image data 2520 may consist of a plurality of segments 2522 to 2524.The plurality of segments 2522 to 2524 may be generated by partitioningthe image data 2520, based on a time. In response to a request from theterminal, the server may transmit the image data 2520 in a unit of asegment to the terminal. Accordingly, when a state of a network betweenthe server and the terminal is changed, a quality of image datarequested for the server by the terminal may be efficiently changed.

However, configurations of the AI data 2510 and the image data 2520 arean example, and configurations of AI data and image data for streamingaccording to embodiments of the disclosure are not limited thereto.

FIG. 26 is a diagram for describing a streaming system 2600, accordingto embodiments of the disclosure.

Referring to FIG. 26, the streaming system 2600 according to embodimentsof the disclosure may include a service server 2610, a plurality ofcontent servers 2622 and 2624 (also referred to as the first contentserver 2622 and the N content server 2624), and a terminal 2630.However, this is an example, and the streaming system 2600 may furtherinclude additional elements. For example, the streaming system 2600 mayinclude a service server 2610. The service server 2610 may be providedin a multiple number. Also, the present embodiment of the disclosurewill now be described with reference to one terminal 2630, but theservice server 2610 and the plurality of content servers 2622 and 2624may stream image data to a plurality of terminals.

The service server 2610 may provide additional information of aplurality of items of image data to the terminal 2630 so as to allow theterminal 2630 to request image data that corresponds to a state of anetwork and is from among the plurality of items of image data. Theadditional information may include respective qualities of the pluralityof items of image data, whether AI encoding has been performed thereon,various DNN configuration information that can be used in performing AIupscaling on image data of a particular quality, or the like. Also, theadditional information may include location information over the networkwhere the plurality of items of image data are stored. For example, theadditional information may include a uniform resource identifier (URI)of the first content server 2622 or the N content server 2624. Also, asdescribed above with reference to FIG. 21, when the plurality of itemsof image data are partitioned in a unit of a segment, the additionalinformation may include an URI of each segment.

The terminal 2630 may request image data that corresponds to the stateof the network and is from among the plurality of items of image data,based on the additional information. For example, according to a resultof determination based on the state of the network, when the terminal2630 determines to request image data of FHD and 5 Mbps generated byperforming AI downscaling, through the 1a DNN, on image data of 8K and30 Mbps from among the plurality of items of image data, the terminal2630 may obtain URI information of the determined image data, based onthe additional information. In the present embodiment of the disclosure,the image data of FHD and 5 Mbps generated by performing AI downscaling,through the 1a DNN, on the image data of 8K and 30 Mbps may be stored inthe first content server 2622. The terminal 2630 may request the firstcontent server 2622 for the determined image data, based on the URIinformation. When a request is received, the first content server 2622may transmit AI data and image data corresponding to the request to theterminal 2630. However, this is an example, and the terminal 2630 mayrequest the first content server 2622 for a request for the determinedimage data in a unit of a segment.

The plurality of content servers 2622 and 2624 may provide informationabout content to the service server 2610. A content server (e.g., thecontent server 2622) may provide information about content stored in thecontent server (e.g., the content server 2622) or information about newadded content to the service server 2610. The service server 2610 maygenerate or update the additional information, based on a plurality ofpieces of information about content which are provided from theplurality of content servers 2622 and 2624.

The streaming system 2600 described above with reference to FIG. 26 isan example, and a system that performs streaming according to thedisclosure is not limited thereto. For example, at least one cacheserver to deliver image data and AI data may be further provided betweenthe terminal 2630 and the plurality of content servers 2622 and 2624. Asanother example, the streaming system 2600 may further include a sourceserver for providing content to each of the plurality of content servers2622 and 2624.

FIG. 27 is a block diagram illustrating a configuration of a server2700, according to embodiments of the disclosure.

Referring to FIG. 27, the server 2700 according to embodiments of thedisclosure may include a communication interface 2710, a processor 2720,and a memory 2730. However, this is an example, and the server 2700 mayadditionally further include other elements. For example, the server2700 may include a plurality of processors.

The communication interface 2710 according to embodiments of thedisclosure may provide an interface for communicating with anotherdevice (e.g., a terminal). The communication interface 2710 may receivea request for additional information or image data from the terminal.Also, the communication interface 2710 may transmit the additionalinformation or media to the terminal.

The processor 2720 according to embodiments of the disclosure maygenerally control the server 2700 to execute one or more programs storedin the memory 2730 to perform operations related to an image encodingapparatus described above with reference to FIGS. 1 to 11, andoperations related to a server described above with reference to FIGS.12 to 26.

The memory 2730 according to embodiments of the disclosure may storevarious data, programs, or applications for driving and controlling theserver 2700. Each of the one or more programs stored in the memory 2730may include one or more instructions. Each program (one or moreinstructions) or each application, which is stored in the memory 2730,may be executed by the processor 2720.

FIG. 28 is a block diagram illustrating a configuration of a terminal2800, according to embodiments of the disclosure.

Referring to FIG. 28, the terminal 2800 according to embodiments of thedisclosure may include a communication interface 2810, a processor 2820,and a memory 2830. However, this is an example, and the terminal 2800may additionally further include other elements. For example, theterminal 2800 may include a plurality of processors including a centralprocessing unit (CPU), a graphic processing unit (GPU), a neutralprocessing unit (NPU), or the like.

The communication interface 2810 according to embodiments of thedisclosure may provide an interface for communicating with anotherdevice (e.g., a server). The communication interface 2810 may transmit arequest for additional information or image data to a server. Also, thecommunication interface 2810 may receive additional information or mediafrom the server and may output the received additional information orthe media to the processor 2820.

The processor 2820 according to embodiments of the disclosure maygenerally control the terminal 2800 to execute one or more programsstored in the memory 2830 to perform operations related to an imagedecoding apparatus described above with reference to FIGS. 1 to 11, andoperations related to a terminal described above with reference to FIGS.12 to 26.

For example, the processor 2820 may transmit a request for additionalinformation of a plurality of items of image data of different qualitiesto the server via the communication interface 2810. In response to therequest, the processor 2820 may obtain the additional information fromthe server via the communication interface 2810.

The processor 2820 may transmit a request for predefined image data fromamong the plurality of items of image data, based on the additionalinformation, to the server via the communication interface 2810. Whenthe processor 2820 obtains image data and AI data that correspond to therequest, the processor 2820 may determine whether to perform AIupscaling on the received image data, based on the AI data. Based on aresult of determining whether to perform AI upscaling, the processor2820 may perform AI upscaling on the received image data through the DNNfor upscaling trained jointly with the DNN for downscaling of theserver.

The processor 2820 may confirm a state of a network, based on a BER or atimestamp of the image data received from the server via thecommunication interface 2810. Based on the confirmed state of thenetwork, the processor 2820 may transmit a request for image data of adifferent quality from among the plurality of items of image data, basedon the additional information, to the server via the communicationinterface 2810. The processor 2820 may obtain the image data and AI datacorresponding to the request.

When the terminal 2800 includes a plurality of processors, each of theprocessors may perform at least some of operations of the processor2820. For example, the CPU may confirm the state of the network and mayrequest image data corresponding thereto. The NPU may perform AIupscaling on AI-encoded image data, and the GPU may perform a processother than a process performed by the NPU, the processes being includedin the AI decoding process described with reference to FIG. 2, or maysupport the NPU in performing the process so as to accelerate theprocess performed by the NPU. However, this is a an example, andoperations to be performed by the processors are not limited to theaforementioned examples.

The memory 2830 according to embodiments of the disclosure may storevarious data, programs, or applications for driving and controlling theterminal 2800. Each of the one or more programs stored in the memory2830 may include one or more instructions. Each program (one or moreinstructions) or each application, which is stored in the memory 2830,may be executed by the processor 2820.

Elements in a block diagram may be combined, an element may be addedthereto, or at least one of the elements may be omitted according toactual specifications of an apparatus. That is, at least two elementsmay be combined to one element, or one element may be divided into twoelements when necessary. Also, functions performed by each element arefor describing the embodiments of the disclosure, and detailedoperations or devices do not limit the scope of the disclosure.

The aforementioned embodiments of the disclosure may be written ascomputer-executable programs that may be stored in a medium.

The medium may continuously store the computer-executable programs, ormay temporarily store the computer-executable programs for execution ordownloading. Also, the medium may be any one of various recording mediaor storage media in which a single piece or plurality of pieces ofhardware are combined, and the medium is not limited to a mediumdirectly connected to a computer system, but may be distributed over anetwork. Examples of the medium include magnetic media, such as a harddisk, a floppy disk, and a magnetic tape, optical recording media, suchas CD-ROM and DVD, magneto-optical media such as a floptical disk, andROM, RAM, and a flash memory, which are configured to store programinstructions. Other examples of the medium include recording media andstorage media managed by application stores distributing applications orby websites, servers, and the like supplying or distributing othervarious types of software.

A model related to the DNN described above may be implemented as asoftware module. When the DNN model is implemented as a software module(for example, a program module including instructions), the DNN modelmay be stored in a computer-readable recording medium.

Also, the DNN model may be a part of at least one of the image decodingapparatus, the image encoding apparatus, the server, or the terminaldescribed above by being integrated as a hardware chip. For example, theDNN model may be manufactured as an exclusive hardware chip for AI, ormay be manufactured as a part of an existing general-purpose processor(for example, CPU or AP) or a graphic-exclusive processor (for exampleGPU).

Also, the DNN model may be provided as downloadable software. A computerprogram product may include a product (for example, a downloadableapplication) as a software program electronically distributed through amanufacturer or an electronic market. For electronic distribution, atleast a part of the software program may be stored in a storage mediumor may be temporarily generated. In this case, the storage medium may bea server of the manufacturer or electronic market, or a storage mediumof a relay server.

The method and apparatus for streaming data according to embodiments ofthe disclosure may transceive AI-encoded image data by using a DNN,based on a state of a network, and thus may constantly maintain QoS ofreproduction of image data in a state of the network which ischangeable.

The effects that may be achieved by the method and apparatus forstreaming data according to embodiments of the disclosure are notlimited to the aforementioned features, and other unstated effects willbe clearly understood by one of ordinary skill in the art in view ofdescriptions below.

While one or more embodiments of the disclosure have been described withreference to the figures, it will be understood by one of ordinary skillin the art that various changes in form and details may be made thereinwithout departing from the spirit and scope as defined by the followingclaims.

What is claimed is:
 1. A method of streaming data, the methodcomprising: receiving, from an electronic device, a request for imagedata from among a plurality of image data of different qualities of aserver; in response to the request, transmitting, to the electronicdevice, artificial intelligence (AI) data and image data that has beenAI encoded through a downscaling neural network of the server that istrained jointly with a upscaling neural network of the electronicdevice, the AI data indicating a resolution conversion rate that isapplied to an original image by the downscaling neural network of theserver to obtain the image data; and receiving, from the electronicdevice, a request for image data of a different quality from among theplurality of image data of different qualities and another AI datacorresponding to the image data of the different quality, based at leaston a state of a network between the electronic device and the server. 2.The method of claim 1, wherein the AI data comprises information aboutthe downscaling neural network that has been applied to the AI-encodedimage data.
 3. The method of claim 1, wherein the receiving of therequest for the image data of the different quality comprises receivingthe request for image data that corresponds to the state of the networkand is determined based at least on AI scale conversion information andquality information of each of the plurality of image data of differentqualities, and wherein the AI scale conversion information comprises theresolution conversion rate, a bitrate, and a codec type that are appliedto the original image by the downscaling neural network of the server toobtain the image data.
 4. The method of claim 1, wherein the image dataof the different quality comprises image data that corresponds to thestate of the network and is determined from among the plurality of imagedata of different qualities based at least on capability informationcomprising information indicating whether AI upscaling is supported bythe electronic device and information about an AI upscale levelsupported by the electronic device.
 5. The method of claim 1, furthercomprising providing the electronic device with an identifier of theserver.
 6. A server for streaming data, the server comprising: a memorystoring one or more instructions; and at least one processor configuredto execute the one or more instructions to receive, from an electronicdevice, a request for image data, from among a plurality of image dataof different qualities of a server, in response to the request,transmit, to the electronic device, artificial intelligence (AI) dataand image data that has been AI encoded through a downscaling neuralnetwork that is trained jointly with an upscaling neural network of theelectronic device, the AI data indicating a resolution conversion ratethat is applied to an original image by the downscaling neural networkof the server to obtain the image data, and receive, from the electronicdevice, a request for image data of a different quality from among theplurality of image data of different qualities and another AI datacorresponding to the image data of the different quality, based at leaston a state of a network between the electronic device and the server. 7.The server of claim 6, wherein the AI data comprises information aboutthe downscaling neural network that has been applied to the AI-encodedimage data.
 8. The server of claim 6, wherein the at least one processoris further configured to execute the one or more instructions to receivethe request for the second version of the image content that correspondsto the state of the network and is determined based at least on AI scaleconversion information and quality information of each of the pluralityof different versions of the image content, and wherein the AI scaleconversion information comprises the resolution conversion rate, abitrate, and a codec type that are applied to the original image by thedownscaling neural network of the server to obtain the image data. 9.The server of claim 6, wherein the image data of the different qualitycomprises image data that corresponds to the state of the network and isdetermined based at least on capability information comprisinginformation indicating whether AI upscaling is supported by theelectronic device and information about an AI upscale level supported bythe electronic device.
 10. The server of claim 6, wherein the at leastone processor is further configured to execute the one or moreinstructions to provide the electronic device with an identifier of theserver.
 11. A non-transitory computer-readable recording medium havingrecorded thereon a program for executing the method of claim 1.